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<p>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<i> in vitro</i> 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 <i>in vitro</i> chemotherapy sensitivity data with mutational profiles indicated that <i>DNMT3A</i> mutations were linked to greater resistance to venetoclax, whereas <i>CEBPA</i> 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 <i>DNMT3A</i>-mutant cases and a trend toward higher CR rates in <i>CEBPA</i>-mutant cases. Furthermore, multivariate Cox regression analysis identified multi-resistance in PharmaFlow stratification as an independent risk factor for overall survival (OS). High-throughput <i>ex vivo</i> 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.</p>

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Integrating ex vivo drug sensitivity and genetic mutation analysis improves prediction of chemotherapy response in acute myeloid leukemia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Wěi Li,
  • Min Ji,
  • Wei Li,
  • Ying Zhou,
  • Ruinan Jia,
  • Xin Wen,
  • Shumin Jin,
  • Yiping Hao,
  • Min Dai,
  • Shumei Xu,
  • Qirui Zhou,
  • Huihui Jiang,
  • Chunyan Ji,
  • Jingjing Ye

摘要

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.