Evidence map›Paper›PMID 41318446›Full record

ArticleGenome medicine2025

Single-cell transcriptome analysis defines novel molecular subtypes and reveals therapeutic implications of T/myeloid mixed-phenotype acute leukemia.

Bin Huang, Wenjie Liu, Yuxin Du, Ping Liu, Zixing Lu, Shiyang Zhong, Xingfei Hu, Wanting Zhou, Yuzhu Shi, Runheng Huang and 18 more

Abstract read
In one paragraph

Article in Genome medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Bin Huang *School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210000, China.
Wenjie Liu *Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.
Yuxin Du *The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, 210002, China.
Ping Liu *Department of Hematology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu, 214000, China.
Zixing LuDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.
Shiyang ZhongDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Xingfei HuDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Wanting ZhouDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Yuzhu ShiDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Runheng HuangDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Xian ZhangDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Jinning ShiDepartment of Hematology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Chuanyang LuDepartment of Hematology, Northern Jiangsu Institute of Clinical Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China.
Chunling WangDepartment of Hematology, Northern Jiangsu Institute of Clinical Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China.
Liang YuDepartment of Hematology, Northern Jiangsu Institute of Clinical Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, 223300, China.
Lingxiang WuDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Wei WuDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Peng XiaDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Qian SunDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.
Liwei ZhuDepartment of Hematology, Sir Run Run Hospital Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Zhi WangDepartment of Hematology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu, 214000, China.
Ruohan ZhangDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Xinrui LinDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Sali LvDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
Qianghu WangSchool of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210000, China. wangqh@njmu.edu.cn.
Sixuan QianDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China. qiansx@medmail.com.cn.
Kening LiDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China. likening@njmu.edu.cn.
Ming HongDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China. hongming@jsph.org.cn.

Funding

Jiangsu Health Innovation Team CZ19202502Jiangsu Province Hospital (the First Affiliated Hospital with Nanjing Medical University) Clinical Capacity Enhancement Project JSPH-MA-2022-1National Natural Science Foundation of China 32200590National Natural Science Foundation of China 81972358National Natural Science Foundation of China 82170153Natural Science Foundation of Jiangsu Province BK20210530
6 · The paper itself

Abstract

backgroundT/myeloid mixed-phenotype acute leukemia (T/My MPAL) is a malignant disease characterized by co-expression of lymphoid and myeloid features. The lack of molecular classification of T/My MPAL results in highly heterogeneity in treatment responses and clinical outcomes. Identifying molecular subtypes and developing subtype-specific treatment strategies are crucial for improving prognosis and enabling personalized therapies.

methodsWe constructed a single-cell transcriptomic landscape of T/My MPAL, acute myeloid leukemia (AML), T-cell acute lymphoid leukemia (T-ALL), and normal donors by analyzing nearly 275,000 cells. Malignant cells were identified using lineage-specific markers and healthy reference datasets. By comparing the whole transcriptomic profiles of T/My MPAL malignant cells with those of AML and T-ALL, we defined three distinct subpopulations and uncovered both intra- and inter-tumoral heterogeneity. Subpopulation-specific molecular markers were identified and validated using immunohistochemistry and independent datasets. These markers were further linked to clinical outcomes. Finally, potential subpopulation-specific therapeutic drugs were identified by correlating gene signatures with IC

resultsMalignant cells in T/My MPAL display distinct lineage characteristics and experience differentiation arrest at a more primitive stage compared to other leukemias. Biphenotypic and bilineal MPAL subtypes defined by flow cytometry exhibit similar transcriptomic profiles, indicating the traditional classification based on a limited set of lineage markers is insufficient. Instead, we define three subpopulations of malignant cells in T/My MPAL, including AML-like, T-ALL-like, and a unique subpopulation that shows distinct transcriptional characteristics neither similar to AML nor to ALL. Markers for each subpopulation are identified and further validated by independent datasets and immunohistochemistry. The unique subpopulation exhibits higher stemness and quiescence, with elevated HOPX expression. Notably, patients with higher levels of the unique subpopulation have significantly poorer prognoses. We further computationally screen potential drugs targeting each subpopulation and indicate that Venetoclax could effectively inhibit the unique MPAL subpopulation and help patient achieve complete remission.

conclusionsOur study provides new insights into the molecular heterogeneity and offers personalized diagnostic and therapeutic targets for T/My MPAL patients. These findings offer valuable insights for enhancing patient outcomes and developing personalized treatment strategies.

Indexed as

Gene Expression ProfilingLeukemia, Biphenotypic, AcuteLeukemia, Myeloid, AcuteSingle-Cell AnalysisTranscriptomeBiomarkers, TumorHumansSingle-Cell Gene Expression AnalysisBiomarkers, TumorIntra-tumoral heterogeneityMixed-phenotype acute leukemiaMolecular subtypingPersonalized treatmentSingle-cell transcriptome

Identifiers

PMID41318446
PMCPMC12771788

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.