{"doi":"10.1002/cac2.12500","title":"Mitochondria‐targeted atovaquone promotes anti‐lung cancer immunity by reshaping tumor microenvironment and enhancing energy metabolism of anti‐tumor immune cells","abstract":"Atovaquone (ATO), a mitochondrial inhibitor, has anti-cancer effects [1]. Based on ATO, we developed mitochondria-targeted atovaquone (Mito-ATO) that had even stronger anti-tumor efficacy than ATO [2]. We synthesized Mito-ATO by attaching the bulky triphenylphosphonium (TPP) group to ATO via a ten-carbon alkyl chain (Supplementary file of methods; Supplementary Figure S1). To assess the effects of Mito-ATO on tumor microenvironment, we conducted single-cell RNA-sequencing (scRNA-seq) on treated immune cells from mice having lung tumors either treated with or without Mito-ATO. Seurat was used for clustering and annotation of CD45+ immune cells [3]. The detected lymphoid cell populations were CD8+ T cells, CD4+ T cells, regulatory T cells (Tregs), gamma-delta T (Tgd) cells, B cells, and natural killer (NK) cells; and the myeloid cells identified were macrophages, neutrophils, plasmacytoid dendritic cells (pDCs), conventional dendritic cells (cDCs) and mast cells (Figure 1A-C). Clustering of CD4+ T cells into seven subpopulations, the separation of neutrophils and granulocytic myeloid-derived suppressor cells (G-MDSCs), and the division of macrophages into M1 and M2 subtypes were described in our previous publication [2]. In this study, we further divided CD8+ T cells into four subpopulations, i.e., exhausted CD8+ T (CD8T_Exhausted) cells, memory like CD8+ T (CD8T_MemoryLike) cells, effector memory like CD8+ T (CD8T_EffectorMemory) cells and naive CD8+ T (CD8T_Naive) cells, using the tumor-infiltrating CD8+ lymphocyte state predictor (TILPRED) method [4] (Figure 1D-E). Probability scores computed with TILPRED could discriminate CD8T_Exhausted from CD8T_MemoryLike cells despite overlap between the two subsets on UMAP representation (Supplementary Figure S2). Mito-ATO treatment significantly decreased the proportion of the CD8T_Exhausted cells (7.3% vs. 32.5%, P < 0.001) but increased the proportion of anti-tumor CD8T_EffectorMemory cells as compared with vehicle treatment (37.3% vs. 11.9%, P < 0.001) (Figure 1F). In comparison, the percentages of CD8+ T cells out of total T cells were not different between the two groups (Supplementary Table S1). For validation, we verified that Mito-ATO treatment induced changes in CD8+ T cell repartition by conducting flow cytometry. Mito-ATO treatment significantly increased the percentage of cytotoxic tumor necrosis factor-alpha (TNF-α)+CD8+ T cells and decreased the percentage of programmed cell death protein-1 (PD-1)+ T cell immunoglobulin and mucin domain-containing protein 3 (TIM3)+CD8+ T cells (Supplementary Figure S3). These matched the scRNA-seq results. We also observed a slight trend toward the upregulation of genes involved in CD8+ T cell recruitment: Ccl25, Ccr7, Cxcl10, Cxcr3, Icam1, and S1pr1 (Supplementary Figure S4). Mito-ATO treatment significantly up-regulated oxidative phosphorylation (OXPHOS) activity in four anti-tumor immune cell populations, i.e., CD8T_EffectorMemory cells, CD8T_MemoryLike cells, cytotoxic CD4+ T cells (CD4T_Cytotoxic), and M1 macrophage cells (Figure 1G). Particularly, the upregulated genes after Mito-ATO treatment were significantly enriched for T cell differentiation (Supplementary Figure S5), suggesting that Mito-ATO treatment may induce CD8+ T cell differentiation. In contrast, Mito-ATO treatment significantly down-regulated OXPHOS activity in five pro-tumor immune cell populations, i.e., interleukin-2 receptor subunit alpha (IL2RA)-low CD4+ (CD4IL2RALO) Tregs, G-MDSCs, mast cells, IL2RA-high CD4+ (CD4IL2RAHI) Tregs, and exhausted CD4+ T cells (CD4T_Exhausted) (Figure 1G). The two types of Treg cells were named following the previous practice [2, 5]. Ten metabolic pathways activity changes were similar to OXPHOS activity changes by Mito-ATO treatment in the above immune cell populations. These pathways were glycolysis, the tricarboxylic acid (TCA) cycle, pyruvate metabolism, glutamine metabolism, Complex I, Complex III, Complex V, DNA repair, purine ","journal":"Cancer Communications","year":2023,"id":338968,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9486,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":635003,"name":"Zheng Yin","orcid":"0000-0002-3547-0606","position":1,"is_corresponding":false},{"id":855950,"name":"Mofei Huang","orcid":"0000-0002-4191-2116","position":2,"is_corresponding":false},{"id":239916,"name":"Yian Wang","orcid":"0009-0002-9268-0527","position":3,"is_corresponding":false},{"id":349889,"name":"Micaël Hardy","orcid":"0000-0003-2976-7785","position":4,"is_corresponding":false},{"id":254811,"name":"Balaraman Kalyanaraman","orcid":"0000-0002-9180-8296","position":5,"is_corresponding":false},{"id":97864,"name":"Stephen T.C. Wong","orcid":"0000-0001-9188-6502","position":6,"is_corresponding":false},{"id":20941,"name":"M. James You","orcid":"0000-0002-9111-8068","position":7,"is_corresponding":false},{"id":239915,"name":"Donghai Xiong","orcid":"0009-0004-8713-9765","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T01:10:40.496978Z","pmid":"37930151","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":[]}