Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4
Neoantigens (NeoAg) offer attractive therapeutic targets for directing a patient’s immune response to the immunogenic subset of mutations expressed exclusively by their cancer cells. Despite the specificity with which NeoAg enable tumor recognition, the majority of approaches for their identification rely on purely predictive methods such as calculating the ability of mutated peptides to bind to a patient’s set of HLA molecules. These methods have met with limited success in revealing natural targets present on tumor cells. We have developed a novel HLA-agnostic functional approach to NeoAg identification which combines genomic sequencing with bioinformatic analysis to nominate mutations for subsequent functional analysis using patient’s own T cells in an effort to identify natural responses generated under physiologic conditions. Using this, we identified a missense mutation (V205I) in the ribosomal protein RPS2 that is recognized by CD8+ T cells from tumor-infiltrating lymphocytes (TIL) of a metastatic HPV16+ Head and Neck Squamous Cell Carcinoma lesion. We then performed adoptive cellular therapy (ACT) using either unseparated TIL or those enriched for RPS2 V205I-specific CD8+ T cells and found the latter to be superior in controlling outgrowth of tumor of a PDX cell line generated from this lesion in NSG mice. Finally, we used single-cell transcriptomics to isolate the genes encoding the RPS2-specific TCR and show that it recognizes the mutated peptide bound to HLA-B*07:02. These results demonstrate that high-affinity NeoAg-specific T cell responses can be identified in cancer patients, that ACT of these cells can control tumor growth, and that the relevant TCR can be isolated for use in TCR engineering-based immunotherapy.
Therapeutic benefit to immune checkpoint blockade (ICB) is currently limited to the subset of cancers thought to possess a sufficient tumor mutational burden (TMB) to allow for the spontaneous recognition of neoantigens (NeoAg) by autologous T cells. We explored whether the response of an aggressive low TMB squamous cell tumor to ICB could be improved through combination immunotherapy using functionally defined NeoAg as targets for endogenous CD4+and CD8+T cells. We found that, whereas vaccination with CD4+or CD8+NeoAg alone did not offer prophylactic or therapeutic immunity, vaccines containing NeoAg recognized by both subsets overcame ICB resistance and led to the eradication of large established tumors that contained a subset of PD-L1+tumor-initiating cancer stem cells (tCSC), provided the relevant epitopes were physically linked. Therapeutic CD4+/CD8+T cell NeoAg vaccination produced a modified tumor microenvironment (TME) with increased numbers of NeoAg-specific CD8+T cells existing in progenitor and intermediate exhausted states enabled by combination ICB-mediated intermolecular epitope spreading. The concepts explored herein should be exploited for the development of more potent personalized cancer vaccines that can expand the range of tumors treatable with ICB.
Epitopes that arise from a somatic mutation, so called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. It has however become evident that the vast majority of these neoepitope candidates do not induce a T cell response when tested in vivo or in vitro, i.e. they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. Several studies have used different combinations of immunoinformatic tools such as MHC binding predictions to prioritize the initial set of neoepitopes candidates. The tools to use and thresholds to apply for this prioritization has so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. To establish the appropriate tools and thresholds in the cancer setting, we here curated a set of immunogenic neoepitopes from the published literature and performed detailed analyses to detect what features discriminate immunogenic neoepitopes from a background set of mutated peptides. We experimentally measured the HLA binding affinity of all curated immunogenic neoepitopes. In doing so, we aimed to identify the optimal affinity threshold to effectively identify immunogenic neoepitopes. As a next step, we sought to assess the added value of different immunoinformatics tools, including various HLA binding prediction algorithms, processing prediction, stability prediction, and immunogenicity prediction, to most effectively detect immunogenic neoepitopes. The obtained results are now going to be used to facilitate the development of more accurate prediction algorithms. Citation Format: Zeynep Kosaloglu-Yalcin, Manasa Lanka, John Sidney, Kerrie Vaughan, Jason Greenbaum, Alessandro Sette, Bjoern Peters. Optimizing immunogenicity prediction of neoepitopes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-002. doi:10.1158/1538-7445.AM2017-LB-002
Accurate identification of tumor-specific neoantigens (NeoAg) is essential for the development of effective personalized cancer vaccines and cellular immunotherapies. The success rates for purely computational approaches which rely on predicted HLA-binding have been disappointing, as these generally ignore 85-90% of total mutations and find less than 5% of those selected can be confirmed as T-cell targets. We have developed a novel NeoAg identification platform in which WES and RNAseq metadata is used to nominate mutations for subsequent functional T-cell analysis using autologous PBMC and/or TIL. Applying this platform to tumors of low mutational burden including PDAC, HNSCC, and MSS-CRC, we report that an average of 35% of expressed mutations selected for functional testing can be verified as neoantigens, and that a significant number of these would be missed by HLA-binding algorithms. Responses comprise both type I and type 2 CD4+ and CD8+ effector T-cells recognizing both “passenger” mutations and known activating mutations in driver oncogenes such as KRAS, PIK3CA, and NRAS. Additionally, we have established a single-cell platform for isolation of T-cell receptors (TCR) against these shared recurrent mutations, and have opened a phase 1b clinical trial to evaluate the efficacy of personalized NeoAg vaccination in solid tumors. Citation Format: Stephen Phillip Schoenberger, Aaron M. Miller, Luise A. Sternberg, Leslie Montero Cuencac, Milad Bahmanof, Zeynep Koasaloglu-Yalcin, Manasa Lanka, Ashmitaa Premlal, Pandurangan Vijayanand, Jason Greenbaum, Allesandro Seatte, Ezra E.W. Cohen, Bjoern Peters. Functional identification and therapeutic targeting of tumor neoantigens [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr PR12.
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