{"doi":"10.1002/advs.202201478","title":"Rapid Profiling of Tumor‐Immune Interaction Using Acoustically Assembled Patient‐Derived Cell Clusters","abstract":"Tumor microenvironment crosstalk, in particular interactions between cancer cells, T cells, and myeloid-derived suppressor cells (MDSCs), mediates tumor initiation, progression, and response to treatment. However, current patient-derived models such as tumor organoids and 2D cultures lack some essential niche cell types (e.g., MDSCs) and fail to model complex tumor-immune interactions. Here, the authors present the novel acoustically assembled patient-derived cell clusters (APCCs) that can preserve original tumor/immune cell compositions, model their interactions in 3D microenvironments, and test the treatment responses of primary tumors in a rapid, scalable, and user-friendly manner. By incorporating a large array of 3D acoustic trappings within the extracellular matrix, hundreds of APCCs can be assembled within a petri dish within 2 min. Moreover, the APCCs can preserve sensitive and short-lived (≈1 to 2-day lifespan in vivo) tumor-induced MDSCs and model their dynamic suppression of T cell tumor toxicity for up to 24 h. Finally, using the APCCs, the authors succesully model the combinational therapeutic effect of a multi-kinase inhibitor targeting MDSCs (cabozantinib) and an anti-PD-1 immune checkpoint inhibitor (pembrolizumab). The novel APCCs may hold promising potential in predicting treatment response for personalized cancer adjuvant therapy as well as screening novel cancer immunotherapy and combinational therapy.","journal":"Advanced Science","year":2022,"id":246232,"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":41,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9555,"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":307901,"name":"Zhuhao Wu","orcid":"0000-0002-6278-1944","position":1,"is_corresponding":false},{"id":307897,"name":"Hongwei Cai","orcid":"0000-0002-7067-9050","position":2,"is_corresponding":false},{"id":307899,"name":"Liya Hu","orcid":"0000-0002-8340-8911","position":3,"is_corresponding":false},{"id":728811,"name":"Xiang Li","orcid":"0000-0002-4916-524X","position":4,"is_corresponding":false},{"id":864322,"name":"Connor Kaurich","orcid":"0000-0002-8774-0882","position":5,"is_corresponding":false},{"id":882795,"name":"Jackson Chang","orcid":null,"position":6,"is_corresponding":false},{"id":253483,"name":"Mingxia Gu","orcid":"0000-0001-7405-8473","position":7,"is_corresponding":false},{"id":537938,"name":"Liang Cheng","orcid":"0000-0001-6049-5293","position":8,"is_corresponding":false},{"id":274882,"name":"Xin Lü","orcid":"0000-0002-0284-6478","position":9,"is_corresponding":false},{"id":307903,"name":"Feng Guo","orcid":"0000-0001-9103-3235","position":10,"is_corresponding":false},{"id":307898,"name":"Zheng Ao","orcid":"0000-0003-2569-0346","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T00:23:43.438539Z","pmid":"35611994","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":[]}