A multi-omic single-cell landscape reveals transcription and epigenetic regulatory features of t(8;21) AML.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40707954", "kind": "science-record", "revision": 3, "content_hash": "sha256:970f21457e41dcf024de05267ad88865306113749753e8a2dcb0c84a39e0b774", "created_at": "2026-11-01T20:30:48Z", "updated_at": "2026-11-01T22:14:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A multi-omic single-cell landscape reveals transcription and epigenetic regulatory features of t(8;21) AML.", "doi": "10.1186/s12967-025-06659-0", "pmid": "40707954", "authors": ["Xue-Ping Li", "Yan Gao", "Bai-Tian Zhao", "Yu-Ting Dai", "Jia-Ying Mao", "Yang Liang", "Lu Jiang"], "journal": "Journal of translational medicine", "year": 2025, "abstract": "BACKGROUND: Comprehensive analysis of single-cell transcriptome and chromatin accessibility will contribute to interpret the heterogeneity of acute myeloid leukemia (AML). We hypothesize that integrating single-cell transcriptomic and chromatin accessibility landscapes underlying t(8;21) AML will provide valuable insights into its heterogeneous cellular properties and gene regulatory programs.\nMETHODS: Here, we conducted paired single-cell RNA-sequencing (scRNA-seq) and single-cell ATAC sequencing on bone marrow samples from newly diagnosed t(8;21) AML patients and healthy controls. Genetic signatures extracted from scRNA-seq were built and validated across three independent cohorts (German AMLCG1999, GSE106291 and TCGA LAML).\nRESULTS: We identified TCF12, a core component of AML1-ETO-containing transcription factor complex (AETFC), as the most active transcription factor in blast cells, driving a universally repressed chromatin state. Furthermore, we delineated two functionally distinct T cell subsets, revealing that EOMES-mediated transcriptional regulation promotes the expansion of a cytotoxic T cell population (T cells_2; high GNLY, NKG7 and GZMB expression), with an increased clonality and a tendency for drug resistance. In addition, we discovered a novel leukemic CMP-like cluster characterized by high TPSAB1, HPGD and FCER1A expression. Leveraging machine learning-based integration of multi-omic profiles, we identified a robust 9-gene prognostic signature, which demonstrated significant predictive value for AML outcomes across three independent cohorts.\nCONCLUSIONS: This multi-omics study provides unprecedented insights into the transcriptional and epigenetic heterogeneity of t(8;21) AML, providing a potential actionable tool for clinical risk stratification.", "claims": [{"id": "claim-method", "statement": "By employing LASSO regression analysis, we constructed a leuk_CMP-like score derived from the estimated proportions of CMP-like cells in each patient.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40707954", "locator": "sections[19] \"Accessibility of CMP-like leukemia cluster in primary t(8;21) AML\" paragraph 4", "quote": "By employing LASSO regression analysis, we constructed a leuk_CMP-like score derived from the estimated proportions of CMP-like cells in each patient."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw sequencing data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center (https://ngdc.cncb.ac.cn/gsa-human), under the accession number #HRA007073. Due to the legal restrictions, these data are under controlled access. Requests for access to these data for research purposes can be directed to the corresponding author Dr. Lu Jiang (jl11891@rjh.com.cn).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40707954", "locator": "sections[12] \"Data availability\" paragraph 2", "quote": "The raw sequencing data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center (https://ngdc.cncb.ac.cn/gsa-human), under the accession number #HRA007073. Due to the legal restrictions, these data are under controlled access. Requests for access to these data for research purposes can be directed to the corresponding author Dr. Lu Jiang (jl11891@rjh.com.cn)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Leveraging machine learning-based integration of multi-omic profiles, we identified a robust 9-gene prognostic signature, which demonstrated significant predictive value for AML outcomes across three independent cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40707954", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Leveraging machine learning-based integration of multi-omic profiles, we identified a robust 9-gene prognostic signature, which demonstrated significant predictive value for AML outcomes across three independent cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Leveraging machine learning-based integration of multi-omic profiles, we identified a robust 9-gene prognostic signature, which demonstrated significant predictive value for AML outcomes across three independent cohorts.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40707954"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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