Neoantigen Prioritization Research Articles

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

NeoHunter: Flexible software for systematically detecting neoantigens from sequencing data

AbstractComplicated molecular alterations in tumors generate various mutant peptides. Some of these mutant peptides can be presented to the cell surface and then elicit immune responses, and such mutant peptides are called neoantigens. Accurate detection of neoantigens could help to design personalized cancer vaccines. Although some computational frameworks for neoantigen detection have been proposed, most of them can only detect SNV‐ and indel‐derived neoantigens. In addition, current frameworks adopt oversimplified neoantigen prioritization strategies. These factors hinder the comprehensive and effective detection of neoantigens. We developed NeoHunter, flexible software to systematically detect and prioritize neoantigens from sequencing data in different formats. NeoHunter can detect not only SNV‐ and indel‐derived neoantigens but also gene fusion‐ and aberrant splicing‐derived neoantigens. NeoHunter supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate neoantigens. These strategies utilize binding characteristics, existing biological big data, and T‐cell receptor specificity to ensure accurate detection and prioritization. We applied NeoHunter to the TESLA dataset, cohorts of melanoma and non‐small cell lung cancer patients. NeoHunter achieved high performance across the TESLA cancer patients and detected 79% (27 out of 34) of validated neoantigens in total. SNV‐ and indel‐derived neoantigens accounted for 90% of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%. Gene fusion‐derived neoantigens were detected in one patient. NeoHunter is a powerful tool to ‘catch all’ neoantigens and is available for free academic use on Github (XuegongLab/NeoHunter).

84P T cell receptor repertoire profiles of tumor -infiltrating lymphocytes improves neoantigen prioritization for personalized cancer immunotherapy

84P T cell receptor repertoire profiles of tumor -infiltrating lymphocytes improves neoantigen prioritization for personalized cancer immunotherapy

Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction

The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous Tcells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.

Determinants of Cancer Neoantigen Immunogenicity

Genetic instability is a hallmark of cancer, and can lead to accumulation of hundreds of somatic mutations. These mutations, which are unique to each patient, have the potential to be immunogenic if presented on major histocompatibility complex (MHC) molecules and recognized as “non-self” neoantigens by the immune system. There is now compelling evidence that neoantigens have an important role in driving anti-tumor immunity. Mutational load correlates with clinical response to immune checkpoint blockade therapy in cancer patients. In addition, neoantigen-directed tumor infiltrating lymphocyte therapy was shown to lead to regression of metastatic lesions in patients. The recent advent of next generation sequencing technologies has enabled rapid and comprehensive discovery of cancer mutations in individual patients and provides the opportunity to develop novel approaches, including vaccines, to selectively target neoantigens. A major challenge, however, is that only a small fraction of the mutations generate T cell responses. Currently MHCI binding prediction is used as a major criterion for prioritizing neoantigens. Although this approach enriches for immunogenic neoantigens, a high frequency of false positive remains. For a mutant peptide to be immunogenic, the source protein has to be processed, the resulting mutant peptide needs to bind MHCI, and finally T cells must recognize the MHCI/peptide complex. To investigate the determinants of neoantigen immunogenicity we performed profiling of the immunopeptidome by mass spectrometry. We also assessed the immunogenicity of single amino acid mutations identified in preclinical cancer models by vaccination. We identified novel features of neoantigen immunogenicity that were validated using human data sets. Furthermore we showed that using these determinants improves neoantigen prioritization.

Enzymatic processing and MHC loading in cancer immunotherapyAdvanced immunopeptidomics based discovery engine for the development of personalized cancer immunotherapy

The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Mutated human leukocyte antigen binding peptides (HLAp) are currently the leading targets. Most studies attempt to identify neoantigens based on predicted affinity to HLA molecules. We have shown that the direct identification of tissue-derived neoantigens by mass spectrometry is becoming feasible and we have recently designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides that is suitable for both cell lines and tissues. The massive amount of HLAp data we acquire while hunting down the neo-antigens is highly valuable. We have compiled a large immunopeptidomics database across dozens of cell types and HLA allotypes. First, we have shown that by taking advantage of co-occurring HLA-I alleles across dozens of immunopeptidomics datasets we can rapidly and accurately identify HLA-I binding motifs. Consequently, training HLA-I ligand predictors on refined motifs significantly improves the identification of neoantigens. Recently, we have acquired the largest reported high-quality HLA-II peptidomics dataset. We introduced completely novel algorithmic tools to analyze such data and developed for the first time HLA-II epitope prediction tool trained on peptidomics data that results in major improvements in prediction accuracy. Second, our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features. We have shown as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, immunopeptidomics facilitates direct identification of neoantigens and it can also improve the prediction of clinically relevant neoantigens, and we develop a novel clinical pipeline for target selection for personalized anti-cancer vaccines.

Abstract 5036: pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection

Abstract Neoantigen vaccines have demonstrated strong efficacy in the treatment of cancers in the realm of personalized medicine when used in combination with checkpoint blockade therapy. Numerous clinical trials involving these therapies are underway across the world. Accurate identification and prioritization of neoantigens is highly relevant to the design of these trials, and for predicting treatment response and understanding mechanisms of resistance. With the advent of whole exome and RNA sequencing technologies, researchers and clinicians are now able to computationally predict neoantigens and design personalized vaccines based on patient-specific mutation information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized medicine, including somatic variant identification and expression, peptide processing and transportation, peptide-MHC binding and stability, recognition by cytotoxic T cells, etc. There has been a rapid development of computational tools that attempt to account for these complexities. Pipelines have been developed to allow researchers to run an ensemble of many potentially informative tools for individual patients. However, output results from these complex pipelines are often unorganized, difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. Additionally, while current pipelines analyze neoantigens from the perspective of variants, transcripts and peptides, they fail to present the underlying details for efficient prioritization of the candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also shown 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 of neoantigen features, presenting all relevant information to experimentalists and immunogenomics tumor boards for candidate selection is a difficult challenge. In response, we have created pVACview, an interactive tool designed to aid in the prioritization and selections of neoantigen candidates for personalized 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 neoantigen candidates across three different levels, including variant, transcript and peptide level information. We hope pVACview will allow researchers and clinicians to analyze and prioritize neoantigen candidates with greater efficiency and accuracy. The application is available as part of the pVACtools pipeline at https://github.com/griffithlab/pVACtools and the online server can be accessed at pvacview.org. Citation Format: Huiming Xia, Susanna Kiwala, Zachary L. Skidmore, Bryan Fisk, S. Peter Goedegebuure, Christopher A. Miller, William E. Gillanders, Obi L. Griffith, Malachi Griffith. pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5036.

Abstract 5639: Computational prediction of MHC anchor locations guide neoantigen prediction and prioritization

Abstract Neoantigens are novel peptide sequences resulting from somatic mutations in tumors that upon loading onto major histocompatibility complex (MHC) molecules allow recognition by T cells. Accurate neoantigen identification is thus critical for designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly inferring whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are SNVs, changes between wildtype and mutant peptide are subtle and require cautious interpretation. An important, yet underappreciated, variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific HLA alleles. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. However, a systematic method for determining anchor locations for the wide range of HLA alleles present in the population and application of these to evaluate MT/WT peptide pairs arising in tumors has not been reported. As a result, many neoantigen studies have either failed to adequately consider this crucial factor or have used conventional assumptions to guide their neoantigen identification process. Here, we provide a computational workflow for predicting anchor locations for a wide range of HLA alleles, using a reference dataset generated from clinical and The Cancer Genome Atlas (TCGA) patient samples. We calculated high probability anchor positions for different peptide lengths for over 300 common HLA alleles. Analysis of these results showed clusters of different anchor trends among the HLA alleles analyzed. A subset of these HLA anchor results were orthogonally validated using protein crystallography structures. Analysis of 923 tumor samples showed that 7-41% of neoantigen candidates were potentially misclassified in the neoantigen selection process and can be rescued using allele-specific knowledge of anchor positions. These anchor predictions are currently undergoing experimental validation using both peptide-MHC stability assays as well as fluorescence-based competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, such as pVACtools, we hope to formalize and streamline the identification process for relevant clinical studies. Citation Format: Huiming Xia, Joshua McMichael, Michelle Becker-Hapak, Onyinyechi C. Onyeador, Rico Buchli, Ethan McClain, Patrick Pence, Suangson Supabphol, Megan M. Richters, Anamika Basu, Cody A. Ramirez, Cristina Puig-Saus, Kelsy C. Cotto, Jasreet Hundal, Susanna Kiwala, S. Peter Goedegebuure, Tanner M. Johanns, Gavin P. Dunn, Antoni Ribas, Christopher A. Miller, William Gillanders, Todd A. Fehniger, Obi L. Griffith, Malachi Griffith. Computational prediction of MHC anchor locations guide neoantigen prediction and prioritization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5639.

NeoScore Integrates Characteristics of the Neoantigen:MHC Class I Interaction and Expression to Accurately Prioritize Immunogenic Neoantigens.

Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by (1) identifying which neoantigen characteristics best predict immunogenicity; (2) integrating these characteristics into an immunogenicity score, the NeoScore; and (3) demonstrating a significant association of the NeoScore with survival in response to immune checkpoint inhibition. One thousand random and evenly split combinations of immunogenic and nonimmunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared with previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high maximum NeoScore was associated with improved survival. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation.

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

GNIFdb: a neoantigen intrinsic feature database for glioma.

Neoantigens are mutation-containing immunogenic peptides from tumor cells. Neoantigen intrinsic features are neoantigens’ sequence-associated features characterized by different amino acid descriptors and physical–chemical properties, which have a crucial function in prioritization of neoantigens with immunogenic potentials and predicting patients with better survival. Different intrinsic features might have functions to varying degrees in evaluating neoantigens’ potentials of immunogenicity. Identification and comparison of intrinsic features among neoantigens are particularly important for developing neoantigen-based personalized immunotherapy. However, there is still no public repository to host the intrinsic features of neoantigens. Therefore, we developed GNIFdb, a glioma neoantigen intrinsic feature database specifically designed for hosting, exploring and visualizing neoantigen and intrinsic features. The database provides a comprehensive repository of computationally predicted Human leukocyte antigen class I (HLA-I) restricted neoantigens and their intrinsic features; a systematic annotation of neoantigens including sequence, neoantigen-associated mutation, gene expression, glioma prognosis, HLA-I subtype and binding affinity between neoantigens and HLA-I; and a genome browser to visualize them in an interactive manner. It represents a valuable resource for the neoantigen research community and is publicly available at http://www.oncoimmunobank.cn/index.php.Database URL http://www.oncoimmunobank.cn/index.php

Beyond Sequencing: Prioritizing and Delivering Neoantigens for Cancer Vaccines.

Neoantigens are tumor-specific proteins and peptides that can be highly immunogenic. Immune-mediated tumor rejection is strongly associated with cytotoxic responses to neoantigen-derived peptides in noncovalent association with self-HLA molecules. Neoantigen-based therapies, such as adoptive T cell transfer, have shown the potential to induce remission of treatment-resistant metastatic disease in select patients. Cancer vaccines are similarly designed to elicit or amplify antigen-specific T cell populations and stimulate directed antitumor immunity, but the selection and prioritization of the neoantigens remains a challenge. Bioinformatic algorithms can predict tumor neoantigens from somatic mutations, insertion-deletions, and other aberrant peptide products, but this often leads to hundreds of potential neoepitopes, all unique for that tumor. Selecting neoantigens for cancer vaccines is complicated by the technical challenges of neoepitope discovery, the diversity of HLA molecules, and intratumoral heterogeneity of passenger mutations leading to immune escape. Despite strong preclinical evidence, few neoantigen cancer vaccines tested in vivo have generated epitope-specific T cell populations, suggesting suboptimal immune system activation. In this chapter, we review factors affecting the prioritization and delivery of candidate neoantigens in the design of therapeutic and preventive cancer vaccines and consider synergism with standard chemotherapies.

Challenges targeting cancer neoantigens in 2021: a systematic literature review

Introduction Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity, while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines. Areas covered In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment. Expert Opinion Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine-learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have significant impact, with the potential to facilitate cancer neoantigen vaccine design.

Abstract 1571: Neoantigen vaccine plus CD38 blockade suppress the proliferation of murine cold lung cancer LLC1

Abstract Immune checkpoint inhibitors are now the mainstay for the treatment of lung cancer. However, so-called cold tumors are known to be resistant to checkpoint inhibitors. Neoantigens associated with genetic mutations of the tumor have the potential to stimulate a potent and highly specific immune response. To develop effective immunotherapy for cold lung cancer, we examined whether neoantigen-based immunotherapy can develop immune responses and display anti-tumor effects on the cold lung cancer model, Lewis lung carcinoma cells (LLC1) resistant to anti-CTLA-4 or anti-PD-1 therapy. LLC1 cells were subjected to whole-exome and RNA sequencing. Mutation-associated neoantigens were predicted and prioritized by their affinity to MHC class I molecules and their expression. Using netMHCpan, sixty mutated peptides with IC50 <200 nM and 68 mutated peptides with IC50>200 nM, but the wild to corresponding mutated peptide ratio of binding affinity>10 were selected as candidate neoantigens. C57BL/6 mice were immunized with dendritic cells pulsed with these neoantigen short peptides (8-10mer). Twenty-five out of 128 short mutated peptides induced peptide-specific CD8+ T cell response to some extent. Next, 25 long peptides (21mer), which incorporated corresponding immunogenic short peptide sequences, were synthesized and used to immunize mice. DC vaccines pulsed with neoantigen in long peptide format (LP) induced both CD4+ and CD8+ T cell response ex vivo. Of them, DC pulsed with LP82 partly delayed the LLC1 growth in vivo. By RNA-Seq, CD38 was highly expressed in LLC1. Thereby, an anti-CD38 antibody was administered in LLC1-bearing mice immunized with DC pulsed with LP82. The tumor growth was suppressed in the combination treatment. Although CD38 plays an important role in converting nicotinamide adenine dinucleotide (NAD+) to adenosine, adenosine receptor antagonist could not inhibit the tumor growth in mice immunized with LP82. Alternatively, NAD+ accumulation due to CD38 blockade decreased regulatory T cells in the tumor immune microenvironment, consistent with the decreased Foxp3+ transcription and increased IFNg and IL2 production of CD4+ T cells sorted from tumor immune microenvironment. Our study suggested that the prioritization of neoantigens that can induce anti-tumor response is critical for developing neoantigen-targeting immunotherapy. Combining an effective neoantigen vaccine and precise immune modulator could enhance anti-tumor activities even in cold lung tumors. Citation Format: Changbo Sun, Koji Nagaoka, Akihiro Hosoi, Yukari Kobayashi, Jun Nakajima, Kazuhiro Kakimi. Neoantigen vaccine plus CD38 blockade suppress the proliferation of murine cold lung cancer LLC1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1571.

Penalized regression analysis identifies features of the peptide:MHC class I interaction and mRNA expression as key to prioritizing neoantigen immunogenicity

Abstract Current approaches to prioritization of tumor-specific neoantigens have low sensitivity and specificity, directly impacting the development of personalized vaccines and the prediction of immunotherapy efficacy. Many biological features have been suggested to contribute to accurate neoantigen prioritization. We calculated predictors for a comprehensive set of biological features for presentation and recognition of neoantigens on a validated neoantigen dataset. One thousand random, evenly-split combinations of immunogenic and non-immunogenic neoantigens were analyzed using penalized regression (lasso) for variable selection. This analysis isolated the peptide:MHC class I dissociation constant, binding stability, and mRNA expression as the key predictive variables of tumor-specific neoantigens. Based on these results, we fit a logistic regression model and tested its performance on three independent datasets. We compared our results to the models by Wells et al., Łuksza et al., and Zhou et al. Our model has improved performance as measured by the area under the receiver operator curve (AUC) compared to Łuksza and Zhou et al. Our model selects the same terms as Wells et al. and performs equivalently. However, it has the advantage of providing a predicted immunogenicity score for each individual neoantigen. Individual antigen approaches are useful for prioritizing vaccine candidates and predicting response to immunotherapy. Future work will focus on the application of this predictor to patient response to immunotherapy.

Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy

ABSTRACT Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the “NP” rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better understand this rule, we solved several neoantigen/MHC structures. These structures showed that neoantigens that follow this rule not only increase peptide-MHC binding affinity but also create new TCR-binding features. These molecular insights highlight the value of immune-based classification in neoantigen studies and may enable the design of more effective cancer immunotherapies.

TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification

BackgroundNeoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice.ResultsWe develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes. The performance of TruNeo and other algorithms were compared based on data from published literature as well as raw data from a lung cancer patient. Recall rate of immunogenic ones among the top 10-ranked neoantigens were compared based on the published combined data set. Recall rate of TruNeo was 52.63%, which was 2.5 times higher than that predicted by MHCflurry (21.05%), and 2 times higher than NetMHCpan 4 (26.32%). Furthermore, the positive rate of top 10-ranked neoantigens for the lung cancer patient were compared, showing a 50% positive rate identified by TruNeo, which was 2.5 times higher than that predicted by MHCflurry (20%).ConclusionsTruNeo, which considers multiple biological processes rather than peptide-MHC binding affinity prediction only, provides prioritization of candidate neoantigens with high immunogenicity for neoantigen-targeting personalized immunotherapies.

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning.

The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity. In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy. Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen. Supplementary data are available at Bioinformatics online.

A novel neoantigen discovery approach based on chromatin high order conformation

BackgroundHigh-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response.MethodsNumerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction.ResultsWe retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach.ConclusionWe believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.