Evidence map›Paper›PMID 42325828›Full record

ArticleBlood and lymphatic cancer : targets and therapy2026

Metabolic Subtypes Predict Treatment Response in Acute Myeloid Leukemia: A Pilot Study.

Changjian Yan, Yan Huang, Qing Cai, Xiang Li, Qinwen Yang, Gang Feng, Huilin Yang, Yanxin Chen, Jianda Hu

Abstract read
In one paragraph

Article in Blood and lymphatic cancer : targets and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Changjian Yan *Department of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.ORCID 0000-0002-1167-070X
Yan Huang *Phase I Clinical Trial Unit, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China.
Qing Cai *Department of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Xiang LiDepartment of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Qinwen YangDepartment of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Gang FengDepartment of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Huilin YangDepartment of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Yanxin ChenDepartment of Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China.
Jianda HuDepartment of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: To establish a dynamic metabolic subtyping system for acute myeloid leukemia (AML) based on longitudinal metabolomics and multi-omics integration, and to evaluate its ability to predict treatment response. Methods: We enrolled 29 AML patients and performed untargeted metabolomics on pre‑ and post-treatment serum samples. Based on finite cyclic combinations of metabolic pathways, pre-treatment patients were classified into G1 (glycolysis/gluconeogenesis/TCA cycle) and G2 (fatty acid/folate biosynthesis). Post‑treatment patients were categorized into TG1 (α-linolenic acid metabolism, pantothenate/CoA biosynthesis) and TG2 (purine metabolism, cysteine/methionine metabolism). Baseline transcriptome data were integrated and validated in three external cohorts (Beat2, GSE6891, GSE37642; total n=994). Single cell and spatial transcriptomics were used to investigate cellular heterogeneity and resistance mechanisms. Results: The complete remission (CR) rate was significantly higher in G2 (71%) than in G1 (29%). After treatment, TG2 showed an 83% CR rate versus 17% in TG1. All patients transitioning from G2 to TG2 achieved CR (100%), whereas those from G1 to TG2 maintained poor response. Baseline metabolic subtype was an independent predictor of treatment response (p<0.05). To explore candidate cellular niches linking G1 metabolic features to chemotherapy resistance, we further integrated single-cell and spatial transcriptomic analyses. The results revealed co-localization of CA2-high immature erythrocytes and neutrophils in non-CR patients. Discussion: Dynamic metabolic subtyping (G1/G2, TG1/TG2) strongly correlates with AML treatment response. The co-localization of CA2-high immature erythrocytes and neutrophils in the bone marrow microenvironment of non-CR patients provides a candidate cellular and microenvironmental correlate of the G1 metabolic phenotype, suggesting a mechanistic link between systemic metabolic reprogramming and chemotherapy resistance. Collectively, these findings highlight the potential of combining dynamic metabolic subtyping with identified microenvironmental features to guide precision therapy.

Indexed as

acute myeloid leukemiametabolic reprogrammingmetabolomicssubtypingtreatment response

Identifiers

PMID42325828
PMCPMC13282956

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