{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(6)"],"submitter":["Turan T"],"funding":["AbbVie"],"pubmed_abstract":["The development of immune checkpoint-based immunotherapies has been a major advancement in the treatment of cancer, with a subset of patients exhibiting durable clinical responses. A predictive biomarker for immunotherapy response is the preexisting T-cell infiltration in the tumor immune microenvironment (TIME). Bulk transcriptomics-based approaches can quantify the degree of T-cell infiltration using deconvolution methods and identify additional markers of inflamed/cold cancers at the bulk level. However, bulk techniques are unable to identify biomarkers of individual cell types. Although single-cell RNA sequencing (scRNA-seq) assays are now being used to profile the TIME, to our knowledge there is no method of identifying patients with a T-cell inflamed TIME from scRNA-seq data. Here, w"],"journal":["Cancer immunology research"],"pagination":["732-746"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10236149"],"repository":["biostudies-literature"],"pubmed_title":["iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-cell RNA-Seq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltration."],"pmcid":["PMC10236149"],"pubmed_authors":["Halliwill K","Binnewies M","Kongpachith S","Reddy D","Turan T","Ye S","McLaughlin RT","Mathew R","Zhao X","Jacob HJ","Samayoa J"],"additional_accession":[]},"is_claimable":false,"name":"iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-cell RNA-Seq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltration.","description":"The development of immune checkpoint-based immunotherapies has been a major advancement in the treatment of cancer, with a subset of patients exhibiting durable clinical responses. A predictive biomarker for immunotherapy response is the preexisting T-cell infiltration in the tumor immune microenvironment (TIME). Bulk transcriptomics-based approaches can quantify the degree of T-cell infiltration using deconvolution methods and identify additional markers of inflamed/cold cancers at the bulk level. However, bulk techniques are unable to identify biomarkers of individual cell types. Although single-cell RNA sequencing (scRNA-seq) assays are now being used to profile the TIME, to our knowledge there is no method of identifying patients with a T-cell inflamed TIME from scRNA-seq data. Here, w","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jun","modification":"2026-05-02T11:14:32.335Z","creation":"2024-12-04T00:24:08.843Z"},"accession":"S-EPMC10236149","cross_references":{"pubmed":["37023414"],"doi":["10.1158/2326-6066.CIR-22-0283"]}}