49 research outputs found
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data
No full textAssay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes
Traité de diagnostic médical, ou Guide clinique pour l'étude des signes caractéristiques des maladies.
No full textEuropeana-GoogleBook
Principes mathématiques de feu Joseph-Anastase da Cunha , traduits littéralement du portugais par J.-M. d'Abreu
No full textAvec mode text
Contemplating immunopeptidomes to better predict them
No full textThe identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity
Predicting MHC-I ligands across alleles and species: how far can we go?
No full textCD8 + T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized vaccines for various diseases including cancer. For a few hundred common human and mouse MHC-I alleles, large datasets of ligands are available and machine learning MHC-I ligand predictors trained on such data reach high prediction accuracy. However, for the vast majority of other MHC-I alleles, no ligand is known.
We capitalize on an expanded architecture of our MHC-I ligand predictor (MixMHCpred3.0) to systematically assess the extent to which predictions of MHC-I ligands can be applied to MHC-I alleles that currently lack known ligand data.
Our results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand prediction accuracy across alleles and species. Robust benchmarking on external data shows that our MHC-I ligand predictor demonstrates competitive performance relative to other state-of-the-art MHC-I ligand predictors and can be used for CD8 + T-cell epitope predictions.
Our work provides a valuable tool for predicting antigen presentation across all human and mouse MHC-I alleles. MixMHCpred3.0 tool is available at https://github.com/GfellerLab/MixMHCpred
Author response image 3. Comparison of the prediction accuracies for EPIC, ISOpure based on all genes and ISOpure based on the subset of signature genes we derived for EPIC.
No full textImmune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).</jats:p
Author response: Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data
No full textInformation et éducation des familles des enfants en situation d’urgence : méthodes d’élaboration des messages
No full textThe MHC Motif Atlas: a database of MHC binding specificities and ligands.
No full textThe highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules
