{"doi":"10.1002/cac2.12635","title":"Vascular smooth muscle cell plasticity in the tumor microenvironment","abstract":"Smooth muscle cell (SMC) plasticity plays a prominent role in the pathogenesis of multiple diseases. This phenomenon is characterized by the loss of canonical SMC marker gene expression (such as Acta2 and Myh11), increased proliferation and migration, and the upregulation of genes typically associated with other cell types, such as macrophages [1-3]. This process is best described in atherosclerosis, where phenotype switching, clonal expansion, and the aberrant expression of inflammatory and matrix proteins contribute to lesion progression and plaque instability [1-4]. However, this phenomenon has not been studied in the context of tumorigenesis. Here, we investigated whether SMC diversity and plasticity play a role in the tumor microenvironment (TME) using well-established SMC-lineage tracing mouse models, single cell RNA sequencing (scRNA-seq), and in silico ligand-receptor predictions. Detailed study methods are described in the supplementary materials and methods section. The goal of this work was to determine if vascular SMC plasticity should be prioritized as a translational target in oncology. Two-colored Myh11 lineage tracing mice have native cells that express tdTomato at baseline. Following tamoxifen administration, any cell expressing MYH11 will lose tdTomato and instead express eGFP (Supplementary Figure S1A-B). Syngeneic colon cancers (MC38) implanted subcutaneously into the flanks of these two-colored mice showed a marked and progressive investment of SMCs into the tumor over an 11-day period (Figure 1A-B, Supplementary Figure S1C). High-resolution fluorescent microscopy revealed the loss of the canonical SMC marker ACTA2 in the eGFP+ lineage traced cells, indicating that they may have been misidentified using traditional histological approaches (Figure 1C). eGFP+ cells were noted far from discernible vasculature within the TME (Figure 1D-E), suggesting their migration away from endothelial networks into the tumor interstitium. Experiments using a separate Rainbow lineage tracer revealed that the expansion of these cells did not occur in a clonal fashion (Supplementary Figure S1D-E) [5]. To more precisely define the diversity of these cells, scRNA-seq was performed. Unbiased clustering and uniform manifold approximation and projection (UMAP) analysis of the tumor data showed the representation of all anticipated cell types, identified by their gene expression profiles (Supplementary Figure S1F). As expected, eGFP-expressing cells were concentrated in the SMC cluster but were also surprisingly prevalent within the larger macrophage cluster (Figure 1F), representing 10% of eGFP+ cells in total. To define the diversity of SMC-derived cells in the TME, all cells expressing an eGFP transcript ≥ 1 were subset and reanalyzed, identifying eight distinct groups of tumor-associated lineage-traced SMCs (Figure 1G). We then used Monocle3 pseudotemporal analysis to map the trajectory of transitioning SMC (Supplementary Figure S1G). The trajectory starts with high contractile gene expression, which diminishes as the SMC adopt a more proliferative and non-traditional phenotype, consistent with our immunofluorescent staining. The cluster furthest from the original contractile cell state appears to take on a ‘macrophage-like’ phenotype, upregulating genes related to antigen presentation and immune response relative to other SMC-derived cells (Figure 1H, Supplementary Figure S1H). Studies using a Dre-Cre reporter, which maps sequential upregulation of macrophage-related genes in SMC-derived cells [4], suggested that this phenomenon was not merely the result of cell fusion (Supplementary Figure S1I-J). These results demonstrate an unexpected level of SMC plasticity in the TME, including the ability to transition toward a macrophage-like cell (macSMC). To understand the mechanism underlying the plasticity from a contractile to a macSMC state, we utilized CellChat to predict ligand-receptor interactions. A comparison of predicted lig","journal":"Cancer Communications","year":2024,"id":474721,"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":1,"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":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":861662,"name":"Richard A. Baylis","orcid":"0000-0001-7040-7920","position":1,"is_corresponding":false},{"id":812552,"name":"Nicolás López","orcid":"0000-0003-0659-4500","position":2,"is_corresponding":false},{"id":558977,"name":"Wei Feng","orcid":"0000-0002-2177-1387","position":3,"is_corresponding":false},{"id":688017,"name":"Hua Gao","orcid":"0000-0002-1029-1476","position":4,"is_corresponding":false},{"id":688018,"name":"Fudi Wang","orcid":"0000-0002-0208-3343","position":5,"is_corresponding":false},{"id":1214630,"name":"Sharika Bamezai","orcid":"0000-0002-2944-740X","position":6,"is_corresponding":false},{"id":1214633,"name":"Changhao Fu","orcid":"0000-0001-7568-3369","position":7,"is_corresponding":false},{"id":236251,"name":"Yoko Kojima","orcid":"0000-0001-9814-2282","position":8,"is_corresponding":false},{"id":303896,"name":"Shaunak Adkar","orcid":"0000-0002-8893-2413","position":9,"is_corresponding":false},{"id":688019,"name":"Lingfeng Luo","orcid":"0000-0002-0601-0053","position":10,"is_corresponding":false},{"id":236712,"name":"Clint L. Miller","orcid":"0000-0003-4276-3607","position":11,"is_corresponding":false},{"id":236255,"name":"Nicholas J. Leeper","orcid":"0000-0002-0905-2806","position":12,"is_corresponding":false},{"id":965255,"name":"Caitlin Bell","orcid":"0000-0001-7352-142X","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T02:06:13.042906Z","pmid":"39648671","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":[]}