Evidence map›Paper›PMID 41265442›Full record

ArticleCell reports. Medicine2025

Precision prediction of venetoclax-azacitidine treatment efficacy in acute myeloid leukemia via integrative drug screening and machine learning.

Peng Jin, Dan Wang, Jie Shen, Qiqi Jin, Hao Zhang, Xiaxin Liu, Mengke He, Wen Jin, Yixuan Li, Fangyi Dong and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

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

25 authors.

Peng JinShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: jinpeng2339@163.com.
Dan WangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Jie ShenShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Qiqi JinKey Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.
Hao ZhangDepartment of Geriatrics and Medical Center on Aging, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Xiaxin LiuShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Mengke HeShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Wen JinShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; CNRS-LIA Hematology and Cancer, Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Yixuan LiDepartment of Mathematics & Computer Science, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA.
Fangyi DongShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Fengbo JinDepartment of Hematology, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei 230022, China.
Yanli YangDepartment of Hematology, The First Affiliated Hospital of Bengbu Medical University, Bengbu 233004, China.
Ruiji ZhengDepartment of Hematology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363000, China.
Shaoyuan WangDepartment of Hematology, Fujian Institute of Hematology, Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou 350001, China.
Jianxin GuoDepartment of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
Shuangyue LiDepartment of Hematology, The Affiliated People's Hospital of Ningbo University, Ningbo 315040, China.
Debin LiuDepartment of Hematology, Quanzhou First Hospital, Fujian Medical University, Quanzhou 362000, China.
Zhiling YanDepartment of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China.
Chenghao JinDepartment of Hematology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China.
Bing XuDepartment of Hematology, Institute of Hematology, School of Medicine, The First Affiliated Hospital of Xiamen University, Key Laboratory of Xiamen for Diagnosis and Treatment of Hematological Malignancy, Xiamen 361003, China.
Weiming GuoDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
Hongming ZhuShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: zhm11931@rjh.com.cn.
Yunxiang ZhangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: zyx12103@rjh.com.cn.
Zhen JinShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: jz12338@rjh.com.cn.
Kankan WangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; CNRS-LIA Hematology and Cancer, Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Shanghai Key Laboratory of Gene Editing and Cell-based Immunotherapy for Hematological Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: kankanwang@shsmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Venetoclax-azacitidine (VEN/AZA) has transformed acute myeloid leukemia (AML) therapy, yet reliable predictors of response remain lacking. We employ a multidisciplinary strategy combining ex vivo drug sensitivity testing, transcriptomic profiling, functional assays, and clinical data to identify determinants of VEN/AZA response. Core genes consistently associated with responsiveness are validated through CRISPR-Cas9 screening, with silencing of BCL2L1 and PINK1 preferentially enhancing drug sensitivity. Building on these insights, we develop and validate an eight-gene random forest model (RF8) that achieves high accuracy across four independent cohorts (n = 498). RF8 distills the downstream effects of genetic alterations to assist in predicting treatment response and outperforms existing genetic mutation-based signatures. Moreover, RF8 scores show a nearly monotonic relationship with clinical response probabilities and survival outcomes, enabling precise patient stratification. These findings demonstrate the feasibility of integrating transcriptomic and drug-response data to guide VEN/AZA therapy, representing an advance toward personalized therapeutic strategies.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsBridged Bicyclo Compounds, HeterocyclicLeukemia, Myeloid, AcuteMachine LearningSulfonamidesDrug Screening Assays, AntitumorFemaleGene Expression ProfilingHumansMaleMiddle AgedPrecision MedicineTreatment OutcomeBridged Bicyclo Compounds, HeterocyclicSulfonamidesvenetoclaxacute myeloid leukemiaex vivo drug sensitivity testingmachine learningrandom forest modeltreatment response predictionvenetoclax-azacitidine

Identifiers

PMID41265442
PMCPMC12765840

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Registered trials

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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.