{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lau MC"],"funding":["NCI NIH HHS"],"pagination":["e1012707"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11849983"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(2)"],"pubmed_abstract":["<h4>Background</h4>Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity.<h4>Methods</h4>We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtyp"],"journal":["PLoS computational biology"],"pubmed_title":["Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment."],"pmcid":["PMC11849983"],"funding_grant_id":["R37 CA225655","R35 CA197735","UM1 CA167552","U01 CA167552","P01 CA087969","P01 CA055075","R50 CA274122","P50 CA127003","R01 CA248857","UM1 CA186107"],"pubmed_authors":["da Silva A","Gu S","Dias Costa A","Hamada T","Lau MC","Vayrynen JP","Ugai T","Tai D","Arima K","Ogino S","Borowsky J","Zhao M","Nguyen MN","Takashima Y","Yeong J","Wu CJ","Fuchs CS","Haruki K","Lennerz JK","Meyerhardt JA","Nowak JA"],"additional_accession":[]},"is_claimable":false,"name":"Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment.","description":"<h4>Background</h4>Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity.<h4>Methods</h4>We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtyp","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2025-04-04T01:12:44.409Z","creation":"2025-04-04T01:12:44.409Z"},"accession":"S-EPMC11849983","cross_references":{"pubmed":["39965007"],"doi":["10.1371/journal.pcbi.1012707"]}}