Purpose: The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community. Experimental Design: We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models. Results: We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia. Conclusions: Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

中文翻译：

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突变模式预测急性髓系白血病的药物敏感性

目的：急性髓系白血病（AML）固有的遗传异质性对精确有效的治疗方法的开发提出了挑战。本研究的目的是阐明耐药性或敏感性的基因组基础，识别药物反应预测的特征，并为研究界提供资源。实验设计：我们对来自 AML 患者的白血病细胞样本进行了靶向测序、高通量药物筛选和单细胞基因组分析。应用统计方法和机器学习模型来识别药物反应预测的特征。我们还整合了大型公共数据集以了解同时发生的突变模式，并进一步研究了单细胞中的突变概况。同时发生或相互排斥模式中揭示的特征进一步受到机器学习模型的影响。结果：我们检测到与特定药物敏感性或耐药性相关的遗传特征，并确定了五个同时发生的突变组。单细胞基因组测序的应用揭示了个体细胞水平上变异的共存，突出了 AML 患者体内不同亚克隆的存在。使用突变模式进行药物反应预测表明，预测某些药物类别的敏感性具有很高的准确性，例如用于 RAS 突变白血病的 MEK 抑制剂。结论：我们的研究强调了考虑基因突变模式对于预测 AML 药物反应的重要性。它提供了一个框架，用于根据突变对 AML 患者进行分类，从而实现药物敏感性预测。