Identification Of Neoantigens Research Articles

Abstract B001: CircRNA-derived neoantigen identification in cancer vaccine development

Abstract Immunotherapy has been a popular topic in combating cancer for its great performance in specificity, versatility and durability recently. Cancer vaccines targeting neoantigens have shown clinical efficacy against cancer, eliciting long-lasting immune response and offering a more selective therapeutic approach with less side effects. However, rare mutation rates and low expression levels of neoantigen make it challenging to identify suitable neoantigens. Traditionally, researchers focus on the canonical mRNA transcriptome and proteome to find potential targets. Recent studies suggest that exploring the non-canonical transcriptome and proteome, particularly circular RNAs (circRNAs), can expand the search. CircRNAs, endogenously formed by the unique back-splicing events during pre-RNA processing, can serve as templates for protein synthesis. Some of these non-canonical peptides are specifically expressed in cancer cells, making circRNAs a valid source for neoantigen discovery. We have developed high-throughput circRNA reporter screening methods and mass spectrometry-based peptidomic workflow to pinpoint translating circRNAs and their derived peptides. Using these techniques, we have identified circRNA-derived neoantigens in breast cancer (BC) cell lines that can be used as possible targets for cancer vaccines. These circRNA-derived neoantigens are immunogenic and can trigger dendritic cell (DC) maturation and T cell activation in vitro. Next, we will test these findings by transferring to in vivo mouse system. We will assess cancer vaccine efficacy by targeting the neoantigens using syngeneic mouse tumor models. Our long- term goal is to apply the circRNA neoantigen identification technology to BC patient samples to facilitate cancer vaccine development and improve cancer immunotherapy. Our work will demonstrate the potential of circRNA-derived non canonical neoantigens in cancer vaccines, serving as a crucial first step to prove their effectiveness in therapy. By expanding our search beyond the traditional transcriptome and proteome, we will explore new targets and transform the neoantigen-based treatment. This innovative effort shifts the current understanding of neoantigen sources and opens up new possibilities for targeted cancer therapy strategies. Citation Format: Yi-Cian Zheng, Chun-Kan Chen. CircRNA-derived neoantigen identification in cancer vaccine development [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B001.

PVACview: an interactive visualization tool for efficient neoantigen prioritization and selection

BackgroundNeoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address.ResultsWe have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information.ConclusionspVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

HLA class II neoantigen presentation for CD4+ T cell surveillance in HLA class II-negative colorectal cancer

ABSTRACT Neoantigen-reactive CD4+ T cells play a key role in the anti-tumor immune response. However, the majority of epithelial tumors are negative for HLA class II (HLA-II) surface expression, and less is known about the processing of HLA-II antigens. Here, we directly identified naturally presented HLA-II neoantigens in HLA-II negative colorectal cancer (CRC) tissue using a proteogenomic approach. The neoantigens were immunogenic and induced patient CD4+ T cells with a Th1-like memory phenotype that produced IFN-γ, IL2 and TNF-α. Multiplex immunohistochemistry (IHC) demonstrated an interaction between Th cells and HLA-II-positive antigen-presenting cells (APCs) at the invasive margin and within the tertiary lymphoid structures (TLS). In our CRC cohort, the density of stromal APCs was associated with HLA-II antigen presentation in the tumor microenvironment (TME), and the number of TLS was positively correlated with the number of somatic mutations in the tumors. These results demonstrate the presence of neoantigen-specific CD4+ surveillance in HLA-II-negative CRC and suggest a potential role for macrophages and dendritic cells (DCs) at the invasive margin and in TLS for antigen presentation. Stromal APCs in the TME can potentially be used as a source for HLA-II neoantigen identification.

MHC-I-presented non-canonical antigens expand the cancer immunotherapy targets in acute myeloid leukemia

Identification of tumor neoantigens is indispensable for the development of cancer immunotherapies. However, we are still lacking knowledge about the potential neoantigens derived from sequences outside protein-coding regions. Here, we comprehensively characterized the immunopeptidome landscape by integrating multi-omics data in acute myeloid leukemia (AML). Both canonical and non-canonical MHC-associated peptides (MAPs) in AML were identified. We found that the quality and characteristics of ncMAPs are comparable or superior to cMAPs, suggesting ncMAPs are indispensable sources for tumor neoantigens. We further proposed a computational framework to prioritize the neoantigens by integrating additional transcriptome and immunopeptidome in normal tissues. Notably, 6 of prioritized 13 neoantigens were derived from ncMAPs. The expressions of corresponding source genes are highly related to infiltrations of immune cells. Finally, a risk model was developed, which exhibited good performance for clinical prognosis in AML. Our findings expand potential cancer immunotherapy targets and provide in-depth insights into AML treatment, laying a new foundation for precision therapies in AML.

Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data. The pipeline consists of a data pre-processing step and three machine learning predictive steps. The pre-processing step analyzes genomic data for different types of alterations, produces a list of all possible antigens, and determines the human leukocyte antigen (HLA) type and T-cell receptor (TCR) repertoire. The first predictive step performs a classification into antigens and neoantigens, selecting neoantigens for further consideration. The next step predicts the strength of binding between neoantigens and available major histocompatibility complexes of class I (MHC-I). The third step is engaged to predict the likelihood of inducing an immune response. Neoepitopes satisfying all three predictive stages are assumed to be potent candidates to ensure immunogenicity. The predictive pipeline is used in two regimes: selecting neoantigens from patients' sequencing data and generating novel neoantigen candidates. Two different techniques - Monte Carlo and Reinforcement Learning - are implemented to facilitate the generativeregime.

TRLS-05. A PILOT STUDY TO ASSESS THE SAFETY, FEASIBILITY, AND PRELIMINARY EFFICACY OF A NEOEPITOPE-BASED PERSONALIZED DNA VACCINE APPROACH IN PEDIATRIC PATIENTS WITH RECURRENT BRAIN TUMORS

Abstract BACKGROUND Pediatric brain tumors present a heterogeneous group of neoplasms with limited treatment options, especially at relapse. Hence, innovative therapeutic strategies are imperative. Immunotherapy, specifically immunogenomics, utilizing personalized vaccines based on tumor-specific neoantigens, has emerged as a promising option in other tumors. However, the feasibility of this approach in pediatric brain tumors remain largely unexplored. METHODS This single institution feasibility study focuses on sequencing analyses of pediatric brain tumors to identify mutant tumor-specific antigens. The study employs tumor/normal exome sequencing and cDNA-capture sequencing to identify and confirm mutant antigens, respectively. An epitope prediction algorithm prioritizes these antigens with subsequent confirmation of binding to HLA class I molecules. The neoantigen DNA vaccine strategy, utilizing a polyepitope DNA vaccine, is designed to target up to 20 prioritized antigens. The TDS-IM v2.0 device facilitates the administration of the vaccine. The primary objective of the study is to determine the feasibility of adjuvant personalized neoantigen DNA vaccine administration in patients with recurrent or refractory pediatric brain tumors. RESULTS We anticipate the identification of actionable neoantigens in pediatric brain tumors to support the potential efficacy of personalized DNA vaccines. The proposed sequencing strategies, epitope prediction algorithm, and in vitrobinding studies aim to establish a comprehensive methodology for neoantigen identification and prioritization. During the meeting, we will present the study background, rationale, detailed methodology, and design to inform the brain tumor community of this study. CONCLUSION This feasibility study is poised to provide essential insights into the development of personalized neoantigen DNA vaccines for recurrent or refractory pediatric brain tumors and aims to demonstrate safety, feasibility, and potential efficacy in targeting mutant tumor-specific antigens. The findings from this study will lay the groundwork for future clinical trials, evaluating the impact of personalized immunotherapy on progression-free and overall survival rates in this challenging patient population.

Targeting Neoantigens in Cancer: Possibilities and Opportunities in Breast Cancer.

As one of the most prevalent forms of cancer worldwide, breast cancer has garnered significant attention within the clinical research setting. While traditional treatment employs a multidisciplinary approach including a variety of therapies such as chemotherapy, hormone therapy, and even surgery, researchers have since directed their attention to the budding role of neoantigens. Neoantigens are defined as tumor-specific antigens that result from a multitude of genetic alterations, the most prevalent of which is the single nucleotide variant. As a result of their foreign nature, neoantigens elicit immune responses upon presentation by Major Histocompatibility Complexes I and II followed by recognition by T cell receptors. Previously, researchers have been able to utilize these immunogenic properties and manufacture neoantigen-specific T-cells and neoantigen vaccines. Within the context of breast cancer, biomarkers such as tumor protein 53 (TP53), Survivin, Partner and Localizer of BRCA2 (PALB2), and protein tyrosine phosphatase receptor T (PTPRT) display exceeding potential to serve as neoantigens. However, despite their seemingly limitless potential, neoantigens must overcome various obstacles if they are to be fairly distributed to patients. For instance, a prolonged period between the identification of a neoantigen and the dispersal of treatment poses a serious risk within the context of breast cancer. Regardless of these current obstacles, it appears highly promising that future research into neoantigens will make an everlasting impact on the health outcomes within the realm of breast cancer. The purpose of this literature review is to comprehensively discuss the etiology of various forms of breast cancer and current treatment modalities followed by the significance of neoantigens in cancer therapeutics and their application to breast cancer. Further, we have discussed the limitations, future directions, and the role of transcriptomics in neoantigen identification and personalized medicine. The concepts discussed in the original and review articles were included in this review article.

The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex

Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUSTCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex.

Neoantigen Identification and Dendritic Cell-Based Vaccines for Lung Cancer Immunotherapy.

Immunotherapies can treat many cancers, including difficult-to-treat cases such as lung cancer. Due to its tolerability, long-lasting therapeutic responses, and efficacy in a wide spectrum of patients, immunotherapy can also help to treat lung cancer, which has few treatment choices. Tumor-specific antigens (TSAs) for cancer vaccinations and T-cell therapies are difficult to discover. Neoantigens (NeoAgs) from genetic mutations, irregular RNA splicing, protein changes, or viral genetic sequences in tumor cells provide a solution. NeoAgs, unlike TSAs, are non-self and can cause an immunological response. Next-generation sequencing (NGS) and bioinformatics can swiftly detect and forecast tumor-specific NeoAgs. Highly immunogenic NeoAgs provide personalized or generalized cancer immunotherapies. Dendritic cells (DCs), which originate and regulate T-cell responses, are widely studied potential immunotherapeutic therapies for lung cancer and other cancers. DC vaccines are stable, reliable, and safe in clinical trials. The purpose of this article is to evaluate the current status, limitations, and prospective clinical applications of DC vaccines, as well as the identification and selection of major histocompatibility complex (MHC) class I and II genes for NeoAgs. Our goal is to explain DC biology and activate DC manipulation to help researchers create extremely potent cancer vaccines for patients.

The Neo-Open Reading Frame Peptides That Comprise the Tumor Framome Are a Rich Source of Neoantigens for Cancer Immunotherapy.

Identification of immunogenic cancer neoantigens as targets for therapy is challenging. Here, we integrate the whole-genome and long-read transcript sequencing of cancers to identify the collection of neo-open reading frame peptides (NOP) expressed in tumors. We termed this collection of NOPs the tumor framome. NOPs represent tumor-specific peptides that are different from wild-type proteins and may be strongly immunogenic. We describe a class of hidden NOPs that derive from structural genomic variants involving an upstream protein coding gene driving expression and translation of noncoding regions of the genome downstream of a rearrangement breakpoint, i.e., where no gene annotation or evidence for transcription exists. The entire collection of NOPs represents a vast number of possible neoantigens particularly in tumors with many structural genomic variants and a low number of missense mutations. We show that NOPs are immunogenic and epitopes derived from NOPs can bind to MHC class I molecules. Finally, we provide evidence for the presence of memory T cells specific for hidden NOPs in peripheral blood from a patient with lung cancer. This work highlights NOPs as a major source of possible neoantigens for personalized cancer immunotherapy and provides a rationale for analyzing the complete cancer genome and transcriptome as a basis for the detection of NOPs.

Tumor neoantigens and tumor immunotherapies.

As a high-risk group of patients with cancer, the elderly exhibit limited efficacy with traditional treatments. Immunotherapy emerges as a promising adjunctive therapeutic approach that holds potential in addressing the needs of geriatric patients with cancer. Neoantigens, a unique class of tumor-specific antigens generated by non-synonymous mutations, are garnering increasing attention as targets for immunotherapy in clinical applications. Newly developed technologies, such as second-generation gene sequencing and mass spectrometry, have provided powerful technical support for the identification and prediction of neoantigens. At present, neoantigen-based immunotherapy has been extensively applied in clinical trials and has demonstrated both safety and efficacy, marking the beginning of a new era for cancer immunotherapy. This article reviews the conception, classification, inducers, and screening process of tumor neoantigens, as well as the application prospects and combination therapy strategies of neoantigen-based cancer immunotherapy.

NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation.

Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

Abstract 5376: Quantitative profiling of HLA class I and class II antigens and neoantigens in tissue biopsy and PBMC samples using an optimized mass spectrometry-based workflow

Abstract Major histocompatibility complex (MHC) molecules play a central role in orchestrating immune responses by presenting antigenic peptides derived from both self and foreign proteins. In the context of cancer, understanding the repertoire of tumor-associated antigens (TAAs) presented by MHC molecules (or HLA molecules in human) is crucial for deciphering how the immune system recognizes and responds to malignant cells. The identification of neoantigens, unique to individual tumors due to somatic mutations, has become a focal point in immunopeptidomic studies. One significant hurdle in systematic immunopeptidomics analysis is the high input material requirement. Here, we present a semi-automated workflow to robustly identify and quantify immunopeptides from reduced amounts of clinical tissue biopsy and peripheral blood mononuclear cell samples. At the core of immunopeptidomics is the enrichment of HLA-associated peptides, followed by identification using mass spectrometry and bioinformatics tools. We optimized the native lysis and a sequential immunoprecipitation workflow for both class I and class II immunopeptides while ensuring scalability and reproducibility. Leveraging the magnetic properties of the beads, 1,000 samples can be processed within a week by a single operator. For both tissue and PBMC samples, we performed a systematic ramping experiment starting with as little as 2.5mg tissue or 5 million PBMCs. In all experiments, The established sample preparation offers high reproducibility and identifications of good quality: 1) Class-I immunopeptides: >60% of the peptides identified are 9-mers, >80% predicted strong binders, and the expected amino acids are enriched at the anchor positions; 2) Class-II immunopeptides, >50% of the peptides identified are 14-to-16-mers, and >50% are predicted strong binders. Furthermore, the pipeline is highly sensitive as we could still identify over 2,800 class-I immunopeptides when processing 2.5 mg fresh frozen tissue and 2,000 - 3,000 class-I immunopeptides when starting from 5 million PBMCs. Furthermore, the developed immunopeptidomics workflow was deployed to profile a cohort of 12 cancerous and matched healthy lung tissue samples. Their Class-I immunopeptidomes clearly displayed a pattern where matched tissues from each subject cluster together, further underlining the fact that the intricate immune-tumor interface is highly personalized. Overall, we established a robust pipeline for for class-I and II immunopeptidome profiling from clinically relevant sample types. Taking advantage of the nature of mass spectrometry-based methods, customized targeted assays can be developed without the need for affinity reagent, allowing specific and absolute quantification of any immunopeptide of interest. Citation Format: Ilja E. Shapiro, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Daniel Redfern, Yuehan Feng, Roland Bruderer, Lukas Reiter. Quantitative profiling of HLA class I and class II antigens and neoantigens in tissue biopsy and PBMC samples using an optimized mass spectrometry-based workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5376.

Abstract 6755: NKT activation enhanced the therapeutic efficacy of neoantigen vaccine in hepatocellular carcinoma

Abstract One sentence introduction: This study indicated the potential of combinational therapy of neoantigen vaccine and iNKT activator α-GalCer in Hepatocellular carcinoma. Hepatocellular carcinoma (HCC) is a leading cause of death around the world. Current immune checkpoint blockade therapies only show effectiveness in some cancer patients, so there is an urgent need for more effective immunotherapy strategies. One alternative is the use of neoantigen-based cancer vaccines, which target tumor-specific antigens (neoantigens) on cancer cells and induce strong T-cell responses. Advances in technology now allow for precise identification of tumor neoantigens using bioinformatic analysis or mass spectrometry. While cancer vaccines have demonstrated strong anti-tumor immune responses and therapeutic effectiveness in some cancer types, their success in HCC remains limited. A contributing factor is the suppressive tumor microenvironment (TME) found in advanced HCC. Some studies suggest that combining neoantigen vaccines with anti-PD1/PD-L1 therapies can significantly enhance HCC treatment outcomes. Overcoming the suppressive immune microenvironment is crucial in cancer treatment, and reactivating intratumoral immunity is essential. In our study, we developed an HCC cancer vaccine using transcriptome sequencing and bioinformatics analysis. Our ELISpot assay results showed that this vaccine induced strong tumor-specific immune responses and significantly inhibited tumor growth in early-stage orthotopic HCC mouse models when administered five days after tumor inoculation. However, the effectiveness decreased in advanced tumors when administered ten days after tumor inoculation. We also discovered a significant relationship between the therapeutic effectiveness of the neoantigen vaccine and the activation of intratumoral NKT cells in HCC. Our study suggested that the type-I NKT cell activator, α-Galactosylceramide (α-GalCer), significantly improved the therapeutic outcomes of the cancer neoantigen vaccine in advanced orthotopic HCC mouse models. In some cases, tumors were almost completely eliminated. This indicates the potential of combining iNKT activators with neoantigen vaccines for HCC treatment. To further understand the role of NKT cells in HCC's TME, we plan to investigate intratumoral NKT cell subtypes following neoantigen vaccine therapy. Additionally, we will develop a new delivery system to deliver type I NKT cell activators and type II NKT inhibitors to tumor sites, aiming to reactivate the suppressive TME in HCC after neoantigen vaccine therapy. Citation Format: Renhao Li, Jing-Chu Hu, Li Rong, Yangfan Wu, Yige He, Jiandong Huang. NKT activation enhanced the therapeutic efficacy of neoantigen vaccine in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6755.

Abstract 4093: Modeling tumor immunoediting under immune selective pressure to inform neoantigen landscape dynamics for effective cancer vaccines

Abstract Tumor specific neoantigens, encoded by somatic mutations, are recognized by T cells, inducing anti-tumor immune responses. This renders neoantigens viable targets for personalized cancer vaccines. However, the identification of immunogenic neoantigens remains suboptimal. Understanding how immune selective pressure-mediated tumor evolution and neoantigen editing contribute to emergence of immunogenic neoantigens may improve prediction accuracy. In this study, we aimed to model tumor evolution and heterogeneity under immune pressure using three preclinical models of lung cancer with common driver mutations, Kras G12D/+ and p53 -/-, namely KPA, KPC, and HKP1, subcutaneously implanted in immunocompetent C57BL/6 (B6) and immune-deficient Rag1-/- mice. Our data suggest that despite shared driver mutations identified by whole exome and RNA-seq, tumor growth in vivo varied between tumor models. KPA tumors were immunogenic, as they were controlled in B6 mice but rapidly progressed in Rag1-/- mice, suggesting immunoediting of KPA-specific neoantigens. In contrast, HKP1 was non-immunogenic, with tumors progressing regardless of immune competency. KPC showed moderate tumor control in B6 mice, which was lost in Rag1-/- mice. We then estimated the cancer cell fraction on every mutation based on phylogenetic reconstruction. Immunogenic KPA tumors had significantly lower mutation burden in B6 than in Rag1-/- mice and the fraction of cell line mutations edited in vivo was significantly higher, suggesting active immunoediting in the former. Non-immunogenic HKP1 tumors did not show significant differences in mutation burden nor fraction of mutations edited between B6 and Rag1-/- mice. The immunogenicity was reflected in the immune infiltrate levels within tumors, with KPA being highly infiltrated by activated CD4 and CD8 T cells compared to KPC. HKP1 tumors showed increased infiltration of suppressive regulatory T cells. T cell receptor (TCR) sequencing on lung tumors showed that clonal expansion is strongly associated with immunogenicity. KPA tumors showed significantly higher TCR clustering with less diversity compared to KPC and more than HKP1. Following a simple approach to study the immunogenicity of shared neoantigens to elicit cross-protective anti-tumor responses, mice were immunized with whole irradiated (IR) tumor cell lines and implanted with live tumor cells (matched and unmatched). While immunization with IR-KPA protected against corresponding tumor implant and moderately against KPC, it did not protect against HKP1, indicating lack of immunogenicity of mutations shared between KPA and HKP1. Further studies testing the anti-tumor responses and characteristics of neoantigens shared between these three related tumor cell lines will inform the conditions under which tumors may escape or regress and help improve neoantigen identifying algorithms. Citation Format: Mariam Mathew George, Jayon Lihm, Hyejin Choi, Yuval Elhanati, Stephen Martis, Marta Luksza, Benjamin Greenbaum, Jedd D. Wolchok, Taha Merghoub. Modeling tumor immunoediting under immune selective pressure to inform neoantigen landscape dynamics for effective cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4093.

Abstract 4356: An isoform-resolution transcriptomic atlas of colorectal cancer from long-read single-cell sequencing

Abstract Colorectal cancer (CRC), a complex and molecularly heterogenous disease that lacks robust predictive markers and targeted therapies, is the second leading cause of cancer death worldwide. In recent years, technological advances in short-read single-cell RNA sequencing (scRNA-seq) have been instrumental in deciphering tumor cell heterogeneities. However, most studies have focused on gene-level expression quantification without considering alterations in transcript structures arising from widespread alternative end processing and/or splicing which are frequently dysregulated in cancer. Here, we combined short- and long-read scRNA-seq of CRC patient samples to build the first isoform-resolution CRC transcriptomic atlas. We identified 394 dysregulated transcript structures in tumor epithelial cells, including 299 derived from the coupling of multiple alternative splicing events. Additionally, we characterized genes and isoforms associated with different colon epithelial lineages and tumor cell subpopulations exhibiting varying levels of stemness and differentiation, as well as their distinct prognostic implications. Finally, we built an algorithm by integrating mass spectrometry data and novel peptides derived from predicted open reading frames of recurrent tumor-specific transcripts to curate a panel of recurring neoepitopes. Overall, this study unveils the transcriptomic landscape of CRC and may drive the identification of neoantigens based on full-length transcriptomic atlases to aid future development of more universal neoantigen-based cancer vaccines. Citation Format: Zhongxiao Li, Bin Zhang, Jia Jia Chan, Hossein Tabatabaeian, Qing Yun Tong, Xiao Hong Chew, Xiaonan Fan, Charlene Chan, Patrick Driguez, Faith Cheong, Shi Wang, Bei En Siew, Ian Jse-Wei Tan, Kai-Yin Lee, Bettina Lieske, Wai-Kit Cheong, Dennis Kappei, Ker-Kan Tan, Xin Gao, Yvonne Tay. An isoform-resolution transcriptomic atlas of colorectal cancer from long-read single-cell sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4356.

Abstract 5888: Novel immune-therapeutic target for lung cancer uncovered by SpliceIO™, an innovative platform that identifies disease-specific alternative splicing

Abstract Alternative splicing (AS) plays a critical role in generating tumor-specific neoantigens targetable through an arsenal of immunotherapeutic modalities. Previous studies have demonstrated tumor-promoting activity of deregulated AS isoforms and mutated splicing factors, both of which increase the yield of neoantigen-encoding AS isoforms. Therefore, the identification of AS isoforms has the potential to produce novel therapeutic targets, especially in lung tumors where splicing deregulation is frequent. Envisagenics has developed SpliceIO, a proprietary software platform for AS-derived neoantigen discovery. SpliceIO uses RNA-seq data to uncover extracellular neoantigens encoded by transmembrane proteins. This predictive ensemble maximizes the value of RNA-seq data to predict fundamental aspects of neoantigen activity, such as protein localization, epitope accessibility, and cancer pathway relevance. Here, we summarize the discovery and validation of novel AS-derived neoantigens identified using SpliceIO in non-small cell lung cancer (NSCLC). Our novel SpliceIO-identified neoantigens in NSCLC were validated following our well-established experimental pipeline. We have confirmed RNA expression of the neoantigen-expressing transcripts in lung cancer cell lines and patient samples using a combination of RT-PCR as well as RNA-ISH. Further, we have validated the membrane-bound extracellular localization of neoantigen-containing isoforms at the protein level using Western blotting, GFP and c-Myc tagged isoform generation, eukaryotic cells transfection, and microscopy. Our top target showed high tumor specificity, heterogeneous distribution in tumor samples, high prevalence, confirmed novel extracellular N-terminal domain, and strong biological relevance in lung cancer. This demonstrates the potential of utilizing RNA splicing for identifying many more membrane associated proteins for tumor specific immunotherapeutic targeting. The combination of computational and experimental approaches to neoantigen identification using SpliceIO allows for novel target discovery amenable for targeted immunotherapeutic development for lung cancer treatment and other indications driven by deregulated AS. Citation Format: Miguel A. Manzanares, Kendall Anderson, Hasan Zumrut, Sakshi Gera, Nayab Habib, Nicole Williams, Shaleigh Stanton, Taylor Floyd, Sanjana Shah, Devon Trahan, Martin Akerman, Gayatri Arun. Novel immune-therapeutic target for lung cancer uncovered by SpliceIO™, an innovative platform that identifies disease-specific alternative splicing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5888.

Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells

Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9–12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.

COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome

BackgroundNeoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality.MethodIn this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides.ResultThe results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens.ConclusionUtilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.