Similar Datasets
Project description:Medulloblastoma is the most frequent malignant primary brain tumor in children. Despite recent advances in integrated genomics, the prognosis in children with high-risk medulloblastoma remains devastating, and new tumor-specific therapeutic approaches are needed. Here, we present an atlas of naturally presented T-cell antigens in medulloblastoma. We mapped the human leukocyte antigen (HLA)-presented peptidomes of 28 tumors and performed comparative immunopeptidome profiling against an in-house benign database. Medulloblastoma proved to be a rich source of novel tumor-associated antigens, naturally presented on HLA class I and II molecules. Remarkably, most tumor-associated peptides and proteins were subgroup-specific, whereas shared presentation among all subgroups of medulloblastoma (WNT, SHH, Group 3 and Group 4) was rare. Functional testing of top-ranking novel candidate antigens demonstrated the induction of peptide-specific T-cell responses, supporting their potential for T-cell immunotherapy. This study is an in-depth mapping of naturally presented T-cell antigens in medulloblastoma. Integration of immunopeptidomics, transcriptomics, and epigenetic data led to the identification of a large set of actionable targets that can be further used for the translation into the clinical setting by facilitating the informed design of immunotherapeutic approaches to children with medulloblastoma.
2024-08-24 | GSE238195 | GEO
Project description:Medulloblastoma is the most frequent malignant primary brain tumor in children. Despite recent advances in integrated genomics, the prognosis in children with high-risk medulloblastoma remains devastating, and new tumor-specific therapeutic approaches are needed. Here, we present an atlas of naturally presented T-cell antigens in medulloblastoma. We mapped the human leukocyte antigen (HLA)-presented peptidomes of 28 tumors and performed comparative immunopeptidome profiling against an in-house benign database. Medulloblastoma proved to be a rich source of novel tumor-associated antigens, naturally presented on HLA class I and II molecules. Remarkably, most tumor-associated peptides and proteins were subgroup-specific, whereas shared presentation among all subgroups of medulloblastoma (WNT, SHH, Group 3 and Group 4) was rare. Functional testing of top-ranking novel candidate antigens demonstrated the induction of peptide-specific T-cell responses, supporting their potential for T-cell immunotherapy. This study is an in-depth mapping of naturally presented T-cell antigens in medulloblastoma. Integration of immunopeptidomics, transcriptomics, and epigenetic data led to the identification of a large set of actionable targets that can be further used for the translation into the clinical setting by facilitating the informed design of immunotherapeutic approaches to children with medulloblastoma.
2024-08-24 | GSE250586 | GEO
Project description:Medulloblastoma is the most frequent malignant primary brain tumor in children. Despite recent advances in integrated genomics, the prognosis in children with high-risk medulloblastoma remains devastating, and new tumor-specific therapeutic approaches are needed. Here, we present an atlas of naturally presented T-cell antigens in medulloblastoma. We mapped the human leukocyte antigen (HLA)-presented peptidomes of 28 tumors and performed comparative immunopeptidome profiling against an in-house benign database. Medulloblastoma proved to be a rich source of novel tumor-associated antigens, naturally presented on HLA class I and II molecules. Remarkably, most tumor-associated peptides and proteins were subgroup-specific, whereas shared presentation among all subgroups of medulloblastoma (WNT, SHH, Group 3 and Group 4) was rare. Functional testing of top-ranking novel candidate antigens demonstrated the induction of peptide-specific T-cell responses, supporting their potential for T-cell immunotherapy. This study is an in-depth mapping of naturally presented T-cell antigens in medulloblastoma. Integration of immunopeptidomics, transcriptomics, and epigenetic data led to the identification of a large set of actionable targets that can be further used for the translation into the clinical setting by facilitating the informed design of immunotherapeutic approaches to children with medulloblastoma.
2024-08-24 | PXD043770 | Pride
Project description:Mapping naturally presented T-cell antigens in medulloblastoma based on integrative multi-omics
| PRJNA1054577 | ENA
Project description:Mapping naturally presented T-cell antigens in medulloblastoma based on integrative multi-omics [RNA-seq]
| PRJNA998354 | ENA
Project description:Mapping naturally presented T-cell antigens in medulloblastoma based on integrative multi-omics [Methylation array]
| PRJNA1054581 | ENA
Project description:Identification of tumor antigens that induce cytotoxic T lymphocytes (CTLs) is crucial for cancer-vaccine development. Despite their predictive ability, current algorithmic approaches and human leukocyte antigen (HLA)-peptidomic analysis allow limited selectivity. Here, we optimized a method to rapidly screen and identify highly immunogenic epitopes that trigger CTL responses. We used a combined application of this method involving immune-specific signature analysis and HLA-associated peptidomics using samples from six patients with triple-negative breast cancer (TNBC) in order to select immunogenic HLA epitopes for in vitro testing. Additionally, we applied high-throughput imaging at the single-cell level in order to confirm the immunoreactivity of the selected peptides. The results indicated that this method enabled identification of promising CTL peptides capable of inducing antitumor immunity. This platform combining high-resolution computational analysis, HLA-peptidomics, and high-throughput immunogenicity testing allowed rapid and robust identification of highly immunogenic epitopes and represents a powerful technique for cancer-vaccine development.
| S-EPMC7125174 | biostudies-literature
Project description:The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.
| S-EPMC7663540 | biostudies-literature
Project description:Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.
| S-EPMC7585874 | biostudies-literature
Project description:This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.
| S-EPMC9922241 | biostudies-literature