{"doi":"10.3389/fimmu.2022.842653","title":"CD33 Expression on Peripheral Blood Monocytes Predicts Efficacy of Anti-PD-1 Immunotherapy Against Non-Small Cell Lung Cancer","abstract":"Non-small cell lung carcinoma (NSCLC) is the leading cause of cancer-related deaths globally. Immune checkpoint blockade (ICB) has transformed cancer medicine, with anti-programmed cell death protein 1 (anti-PD-1) therapy now well-utilized for treating NSCLC. Still, not all patients with NSCLC respond positively to anti-PD-1 therapy, and some patients acquire resistance to treatment. There remains an urgent need to find markers predictive of anti-PD-1 responsiveness. To this end, we performed mass cytometry on peripheral blood mononuclear cells from 26 patients with NSCLC during anti-PD-1 treatment. Patients who responded to anti-PD-1 ICB displayed significantly higher levels of antigen-presenting myeloid cells, including CD9 + nonclassical monocytes, and CD33 hi classical monocytes. Using matched pre-post treatment samples, we found that the baseline pre-treatment frequencies of CD33 hi monocytes predicted patient responsiveness to anti-PD-1 therapy. Moreover, some of these classical and nonclassical monocyte subsets were associated with reduced immunosuppression by T regulatory (CD4 + FOXP3 + CD25 + ) cells in the same patients. Our use of machine learning corroborated the association of specific monocyte markers with responsiveness to ICB. Our work provides a high-dimensional profile of monocytes in NSCLC and links CD33 expression on monocytes with anti-PD-1 effectiveness in patients with NSCLC.","journal":"Frontiers in Immunology","year":2022,"id":254261,"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":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9503,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":798414,"name":"Ahmad Alimadadi","orcid":"0000-0002-7888-5019","position":1,"is_corresponding":false},{"id":299945,"name":"Daniel J. Araujo","orcid":null,"position":2,"is_corresponding":false},{"id":900867,"name":"David J. Barry","orcid":"0000-0003-2763-5244","position":3,"is_corresponding":false},{"id":901381,"name":"Norma A. Gutierrez","orcid":null,"position":4,"is_corresponding":false},{"id":901382,"name":"Max Hardy Werbin","orcid":null,"position":5,"is_corresponding":false},{"id":900868,"name":"Edurne Arriola","orcid":"0000-0001-8960-7519","position":6,"is_corresponding":false},{"id":110451,"name":"Sandip Pravin Patel","orcid":"0000-0002-8387-4840","position":7,"is_corresponding":false},{"id":110384,"name":"Christian H. Ottensmeier","orcid":"0000-0003-3619-1657","position":8,"is_corresponding":false},{"id":271171,"name":"Huy Q. Dinh","orcid":"0000-0002-3307-1126","position":9,"is_corresponding":false},{"id":247023,"name":"Catherine C. Hedrick","orcid":"0000-0003-2045-4117","position":10,"is_corresponding":false},{"id":271175,"name":"Claire Olingy","orcid":"0000-0002-4449-4319","position":0,"is_corresponding":true}],"reference_count":64,"raw_metadata":null,"created_at":"2026-07-19T00:25:03.918844Z","pmid":"35493454","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":[]}