ArticleFunctional & integrative genomics2026
Integrating ex vivo drug sensitivity and genetic mutation analysis improves prediction of chemotherapy response in acute myeloid leukemia.
Article in Functional & integrative genomics, 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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Abstract
Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by extensive genomic alterations and molecular diversity, posing significant therapeutic challenges. Current treatment strategies lack precise predictive biomarkers for chemotherapy response, highlighting the need for tools to guide precision therapy. In this study, we evaluated in vitro chemotherapy sensitivity in bone marrow samples from 98 AML patients using the PharmaFlow platform. Sensitivity to 10 commonly used chemotherapeutic agents (including venetoclax) and 20 combination regimens was assessed. Patients were stratified into three groups based on their PharmaFlow sensitivity profiles: multi-sensitive, intermediate-resistant, and multi-resistant. Analysis of these groups revealed that the rate of complete remission (CR) after one cycle of induction therapy decreased with increasing resistance. Additionally, integration of in vitro chemotherapy sensitivity data with mutational profiles indicated that DNMT3A mutations were linked to greater resistance to venetoclax, whereas CEBPA mutations in the bZIP region were linked to increased sensitivity. External validation in an independent cohort of 56 patients treated with "7 + 3" plus venetoclax confirmed a significantly lower CR rate in DNMT3A-mutant cases and a trend toward higher CR rates in CEBPA-mutant cases. Furthermore, multivariate Cox regression analysis identified multi-resistance in PharmaFlow stratification as an independent risk factor for overall survival (OS). High-throughput ex vivo drug sensitivity testing can help predict induction chemotherapy outcomes in AML. When combined with genetic mutation analysis, it helps identify mutation signatures that respond better to specific treatments, supporting the development of therapeutic strategies.
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