{"doi":"10.1002/1878-0261.13304","title":"Identification and targeting of a <scp>HES1‐YAP1‐CDKN1C</scp> functional interaction in fusion‐negative rhabdomyosarcoma","abstract":"Rhabdomyosarcoma (RMS), a cancer characterized by features of skeletal muscle, is the most common soft-tissue sarcoma of childhood. With 5-year survival rates among high-risk groups at &amp;lt; 30%, new therapeutics are desperately needed. Previously, using a myoblast-based model of fusion-negative RMS (FN-RMS), we found that expression of the Hippo pathway effector transcriptional coactivator YAP1 (YAP1) permitted senescence bypass and subsequent transformation to malignant cells, mimicking FN-RMS. We also found that YAP1 engages in a positive feedback loop with Notch signaling to promote FN-RMS tumorigenesis. However, we could not identify an immediate downstream impact of this Hippo-Notch relationship. Here, we identify a HES1-YAP1-CDKN1C functional interaction, and show that knockdown of the Notch effector HES1 (Hes family BHLH transcription factor 1) impairs growth of multiple FN-RMS cell lines, with knockdown resulting in decreased YAP1 and increased CDKN1C expression. In silico mining of published proteomic and transcriptomic profiles of human RMS patient-derived xenografts revealed the same pattern of HES1-YAP1-CDKN1C expression. Treatment of FN-RMS cells in vitro with the recently described HES1 small-molecule inhibitor, JI130, limited FN-RMS cell growth. Inhibition of HES1 in vivo via conditional expression of a HES1-directed shRNA or JI130 dosing impaired FN-RMS tumor xenograft growth. Lastly, targeted transcriptomic profiling of FN-RMS xenografts in the context of HES1 suppression identified associations between HES1 and RAS-MAPK signaling. In summary, these in vitro and in vivo preclinical studies support the further investigation of HES1 as a therapeutic target in FN-RMS.","journal":"Molecular Oncology","year":2022,"id":282635,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9612,"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":434426,"name":"Kristianne M. Oristian","orcid":"0000-0003-0298-7323","position":1,"is_corresponding":false},{"id":334422,"name":"David G. Kirsch","orcid":"0000-0002-2086-205X","position":2,"is_corresponding":false},{"id":33513,"name":"Rex C. Bentley","orcid":"0000-0002-4947-9150","position":3,"is_corresponding":false},{"id":439774,"name":"Changde Cheng","orcid":"0000-0002-2458-2522","position":4,"is_corresponding":false},{"id":241111,"name":"Xiang Chen","orcid":"0000-0002-2499-8261","position":5,"is_corresponding":false},{"id":331201,"name":"Po‐Han Chen","orcid":"0000-0002-9471-4471","position":6,"is_corresponding":false},{"id":277858,"name":"Jen‐Tsan Chi","orcid":"0000-0003-3433-903X","position":7,"is_corresponding":false},{"id":434429,"name":"Corinne M. Linardic","orcid":"0000-0002-3257-2885","position":8,"is_corresponding":false},{"id":472337,"name":"Alexander R. Kovach","orcid":null,"position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-19T00:29:19.691592Z","pmid":"36037042","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":[]}