{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["27(3)"],"submitter":["Teng PN"],"funding":["Defense Health Agency Consortium"],"pubmed_abstract":["Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or Consensus<sup>TME</sup>. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/."],"journal":["iScience"],"pagination":["109198"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10910246"],"repository":["biostudies-literature"],"pubmed_title":["ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data."],"pmcid":["PMC10910246"],"pubmed_authors":["Schaaf JP","Wilson KN","Raj-Kumar PK","Tarney CM","Maxwell GL","Wilkerson MD","Darcy KM","Olowu V","Edwards M","Litzi TJ","Hunt AL","Park FS","Conrads TP","Teng PN","Abulez T","Oliver J","Chiang A","Hood BL","Phippen NT","Bateman NW","Conrads KA","Mitchell D"],"additional_accession":[]},"is_claimable":false,"name":"ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data.","description":"Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or Consensus<sup>TME</sup>. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-06-01T21:45:19.572Z","creation":"2025-04-19T22:02:31.712Z"},"accession":"S-EPMC10910246","cross_references":{"pubmed":["38439970"],"doi":["10.1016/j.isci.2024.109198"]}}