{"doi":"10.1101/2021.05.25.445197","title":"Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types","abstract":"Abstract The FDA has recently approved high tumor mutational burden (TMB), defined by ≥10 mutations/Mb, as a biomarker for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies testify that high TMB levels are only able to stratify ICI responders in a subset of cancer types, where the mechanisms underlying this observation have remained unknown. We hypothesized that the tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power ) in a cancer type, leading to this observation. To systematically study this hypothesis, we analyzed TCGA expression data to infer the levels of 31 immune-related factors characteristic of the TME of different cancer types. We integrated this information with TMB and response data of 2,277 patients treated with anti-PD1 or anti-PD-L1 ICI to identify the key immune factors that can determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterize cancer types with high TMB power. A model based on these two immune factors is strongly predictive of the TMB power in a given cancer type (Spearman Rho=0.76, P<3.6×10 −04 ). Using this model, we provide predictions of the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available on a large scale. Our analysis indicates that TMB-High may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":216594,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9527,"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":3207,"name":"Sanju Sinha","orcid":"0000-0002-2688-0603","position":1,"is_corresponding":false},{"id":504432,"name":"Cristina Valero","orcid":"0000-0003-3397-2888","position":2,"is_corresponding":false},{"id":322365,"name":"Alejandro A. Schäffer","orcid":"0000-0002-2147-8033","position":3,"is_corresponding":false},{"id":13200,"name":"Kenneth Aldape","orcid":"0000-0001-5119-7550","position":4,"is_corresponding":false},{"id":230059,"name":"Kevin Litchfield","orcid":"0000-0002-3725-0914","position":5,"is_corresponding":false},{"id":69956,"name":"Timothy A. Chan","orcid":"0000-0002-9265-0283","position":6,"is_corresponding":false},{"id":90772,"name":"Luc G.T. Morris","orcid":"0000-0002-4417-2280","position":7,"is_corresponding":false},{"id":440002,"name":"Fiorella Schischlik","orcid":"0000-0003-4299-7657","position":8,"is_corresponding":false},{"id":457252,"name":"Neelam Sinha","orcid":"0000-0001-6496-224X","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-18T23:53:11.245932Z","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":[]}