ArticleDiscover oncology2025
Predicting immunotherapeutic response and therapeutic targets by stemness classification of acute myeloid leukemia by stemness score.
Article in Discover oncology, 2025. 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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Abstract
objectiveThis study aimed to investigate the role of stemness in acute myeloid leukemia (AML), stratify patients into subtypes based on stemness-associated signatures, and explore their prognostic implications as well as potential therapeutic vulnerabilities.
methodsTo investigate the diagnostic and prognostic implications of stemness in AML, we integrated and analyzed comprehensive datasets from the TCGA, GEO, and cBioPorta databases. Initially, stemness and immune scores were calculated using transcriptomic data from patients in the TCGA-LAML training cohort. Unsupervised clustering was then employed to identify two distinct stemness subgroups. Survival analyses were performed for patients in these subgroups using two independent validation cohorts, GSE106291 and OSHU-AML. Furthermore, four complementary machine learning algorithms were employed to evaluate feature importance and identify key stemness-associated genes. Finally, comparative analyses were conducted between the two stemness subgroups to evaluate differences in clinical characteristics, immune cell infiltration patterns, immune scores, expression of immune checkpoint molecules, and predicted responses to therapeutic agents.
resultsOur analysis revealed that patients in stemness subgroup II exhibited poorer prognoses, however, treatment with PD-1 inhibitors demonstrated significant efficacy in this subgroup. Conversely, patients in stemness subgroup I displayed enhanced sensitivity to conventional chemotherapies, including Cytarabine, Methotrexate, and Etoposide, compared to those subgroup II. A striking divergence in mutation profiles was observed between the two subgroups, suggesting the engagement of distinct biological processes. Additionally, we identified eight stemness-related genes as potential biomarkers for therapeutic stratification.
conclusionIn conclusion, we have established two distinct stemness subgroups within AML based on stemness scores, and highlighted their differential responses to immunotherapy and conventional treatments. These findings offer novel insights into clinical stratification and therapeutic targeting in AML, paving the way for more personalized treatment approaches.
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