Ontology highlight
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PROVIDER: PRJNA884260 | ENA |
REPOSITORIES: ENA
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Project description:We proposed a complete workflow for neoantigen identification and validation based solely on MS immunopeptidomics.We first developed a new Neo DiscoveryTM immunopeptidome enrichment Kit, which can be used for HLA peptide purification from a small amount of biopsied tissue as low as 18mg. To achieve high sensitivity with such a low amount of sample, we leveraged the power of deep learning and a massive amount of MS-based immunopeptidomics data to build DeepNovo-HLA, a de novo sequencing model specialized for HLA peptides. We also developed DeepImmu, a personalized model for immunogenicity prediction based on the central tolerance of T cells, i.e. the positive and negative selection of T cells in an individual patient.
2024-05-04 | PXD046370 | Pride
Project description: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.
2023-05-16 | PXD037655 | Pride
Project description:Neoantigens, which are expressed on tumor cells, are one of the main targets of an effective anti-tumor T-cell response. Cancer immunotherapies to target neoantigens are of growing interest, and are currently in early human trials, but methods to identify neoantigens either require invasive or difficult-to-obtain clinical specimens, the screening of hundreds to thousands of synthetic peptides or tandem minigenes or are only relevant to specific human leukocyte antigen (HLA) alleles. We apply deep learning to a large (N=74 patients) HLA peptide and genomic dataset from various human tumors to create a computational model of antigen presentation for neoantigen prediction. We show that our model, named EDGE, increases the positive predictive value of HLA antigen prediction by up to 9 fold. We apply EDGE to enable identification of neoantigens and neoantigen-reactive T cells using routine clinical specimens and small numbers of synthetic peptides for most common HLA alleles. EDGE could enable an improved ability to develop neoantigen-targeted immunotherapies for cancer patients.
2018-07-21 | MSV000082648 | MassIVE
Project description:Neoantigen-reactive cytotoxic T lymphocytes play a vital role in precise cancer cell elimination. In this study, we demonstrate the effectiveness of personalized neoantigen-based T cell therapy in inducing tumor regression in two patients suffering from heavily-burdened metastatic ovarian cancer. Our approach involved the development of a robust pipeline for ex vivo expansion of neoantigen-reactive T lymphocytes. Neoantigen peptides were designed and synthesized based on the somatic mutations of the tumors and their predicted HLA binding affinities. These peptides were then presented to T lymphocytes through co-culture with neoantigen-loaded dendritic cells for ex vivo expansion. Subsequent to cell therapy, both patients exhibited significant reductions in tumor marker levels and experienced substantial tumor regression. One patient achieved repeated cancer regression through infusions of T cell products generated from newly identified neoantigens. Transcriptomic analyses revealed a remarkable increase in neoantigen-reactive cytotoxic lymphocytes in the peripheral blood of the patients following cell therapy. These cytotoxic T lymphocytes expressed polyclonal T cell receptors (TCR) against neoantigens, along with abundant cytotoxic proteins and pro-inflammatory cytokines. The efficacy of neoantigen targeting was significantly associated with the immunogenicity and TCR polyclonality. Notably, the neoantigen-specific TCR clonotypes persisted in the peripheral blood after cell therapy. Our findings indicate that personalized neoantigen-based T cell therapy triggers cytotoxic lymphocytes expressing polyclonal TCR against ovarian cancer, suggesting its promising potential in cancer immunotherapy.
2023-12-01 | GSE248401 | GEO
Project description:Targeting tumor-specific neoantigens is promising for cancer immunotherapy, yet their ultra-low expression on tumor cells poses significant challenges for T cell therapies. Here, we found that chimeric antigen receptors (CARs) exhibited 10-100 times lower sensitivity compared to T cell receptors (TCRs) when targeting p53R175H common neoantigen. To enhance CAR functionality, we introduce T cell receptor fusion construct (TRuC) and synthetic TCR and antigen receptor (STAR). Our data demonstrate that STAR, which incorporates TCR-mimic antibody fragments and complete TCR signaling machinery, optimally reproduces antigen sensitivity of TCRs. STAR outperforms both CAR and TRuC in redirecting both CD8+ and CD4+ T cells to recognize HLA class I neoantigens. In vitro, human primary T cells engineered with STAR kill multiple cancer cell lines with low neoantigen density better than CAR-T and TRuC-T cells. In tumor mouse models, STAR-T cells outperform CAR-T and TRuC-T cells in controlling neoantigen-low breast cancer and leukemia. Taken together, our findings highlight severe defects in CAR sensitivity and introduce STAR as a more sensitive synthetic receptor, providing a new framework for T cell-based immunotherapy targeting tumors with low neoantigen density.
2024-10-31 | GSE279137 | GEO
Project description:Background: Patient derived organoids (PDOs) can be established from colorectal cancers as in vitro models to interrogate cancer biology and its clinical relevance. We applied mass spectrometry (MS) immunopeptidomics to investigate neoantigen presentation and whether this can be augmented through interferon gamma (IFN) or MEK-inhibitors. Methods: Four PDOs from chemotherapy refractory and one from a treatment naïve CRC were expanded to replicates with 100 million cells each, and HLA class I and class II peptide ligands were analysed by MS. Results: We identified an average of 9,936 unique peptides per PDO which compares favourably against published immunopeptidomics studies, suggesting high sensitivity. Loss of heterozygosity of the HLA locus was associated with low peptide diversity in one PDO. Peptides from genes without detectable expression by RNA-sequencing were rarely identified by MS. Only 3 out of 612 non-silent mutations encoded for neoantigens that were detected by MS. Treatment of four PDOs with IFN upregulated HLA class I expression and qualitatively changed the immunopeptidome, with increased presentation of IFN-inducible genes. HLA class II presented peptides increased on average 16-fold with IFN treatment. MEK-inhibitor treatment showed no consistent effect on class I or II HLA expression or the peptidome. Importantly, no additional HLA class I or II presented neoantigens became detectable with any treatment. Conclusions: Only 3 out of 612 non-synonymous mutations encoded for neoantigens that were detectable by MS. Although MS has sensitivity limits and biases, and likely underestimated the true neoantigen burden, this established a lower bound of the percentage of non-silent mutations that encode for presented neoantigens, which may be as low as 0.5%. This could be a reason for the poor responses of non-hypermutated CRCs to immune checkpoint inhibitors. MEK-inhibitors recently failed to improve checkpoint-inhibitor efficacy in CRC and the observed lack of HLA upregulation or improved peptide presentation may explain this.
2019-11-27 | PXD014017 | Pride
Project description:The purpose of this study is 1) to evaluate the feasibility of manufacturing a patient-specific neoantigen cancer vaccine, which involves predicting the patient’s neoantigens and generating a vaccine that encodes the predicted neoantigens; and, 2) to identify and select patients who may be eligible for a shared neoantigen cancer vaccine where their tumor contains a specific shared mutation and who have the correct HLA allele capable of presenting the neoantigen derived from the tumor-specific mutation.
| 2293590 | ecrin-mdr-crc
Project description:Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification
| PRJEB61429 | ENA
Project description:Adoptive cell transfer (ACT) using neoantigen-specific T cells is an effective immunotherapeutic strategy. However, the difficulty in identifying and screening neoantigen-specific T cells limits its widespread application. Here, we prepared neoantigen-reactive T cells (NRTs) after immunization with a tumor lysate-loaded dendritic cell (DC) vaccine (OCDC) for ACT. Our results demonstrated that the OCDC vaccine could induce a neoantigen-specific immune response, and it was feasible to prepare NRTs by loading immunogenic neoantigens onto DCs and coculturing them with spleen lymphocytes from mice immunized with the OCDC vaccine. We then transferred these NRTs back to the LL/2 tumor-bearing mice after OCDC vaccine immunization and found that OCDC vaccine and NRTs adoptive transfer combination treatment could induce a stronger antitumor response. Furthermore, we found that infused NRTs could migrate into the tumor microenvironment to exert antitumor effects. Our research provides a new and convenient method of preparing NRTs for ACT. The clinical translation of this approach has the potential to increase ACT efficacy.
2024-07-05 | GSE271086 | GEO