{"doi":"10.1101/2021.01.20.427515","title":"Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS","abstract":"Abstract The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell-type-specific gene expression in each sample from bulk expression, and LIRICS (LIgand Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand-receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method, CIBERSORTx. Second, analyzing the TCGA, we uncover cell-type-specific interactions of mismatch-repair-deficient tumors that are associated with their higher anti-PD1 response rates, including specific T-cell co-stimulating interactions that enhance immunotherapy response independently of the tumors mutation burden levels. Finally, we identify a subset of ligand-receptor interactions in the melanoma TME that predict patient response to anti-PD1 therapy better than recently published transcriptomics-based methods.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":218304,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9524,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":816114,"name":"Sushant Patkar","orcid":"0000-0002-5877-0599","position":1,"is_corresponding":false},{"id":3194,"name":"Joo Sang Lee","orcid":"0000-0001-8564-0848","position":2,"is_corresponding":false},{"id":409247,"name":"E. Michael Gertz","orcid":"0000-0001-8390-4387","position":3,"is_corresponding":false},{"id":3206,"name":"Welles Robinson","orcid":"0000-0001-9018-6811","position":4,"is_corresponding":false},{"id":440002,"name":"Fiorella Schischlik","orcid":"0000-0003-4299-7657","position":5,"is_corresponding":false},{"id":816115,"name":"David R. Crawford","orcid":"0000-0001-8947-0057","position":6,"is_corresponding":false},{"id":322365,"name":"Alejandro A. Schäffer","orcid":"0000-0002-2147-8033","position":7,"is_corresponding":false},{"id":3227,"name":"Eytan Ruppin","orcid":"0000-0002-7862-3940","position":8,"is_corresponding":false},{"id":616275,"name":"Kun Wang","orcid":"0000-0001-8923-8557","position":0,"is_corresponding":true}],"reference_count":77,"raw_metadata":null,"created_at":"2026-07-18T23:53:29.626149Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}