A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]

We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents. Overall design: We measured the gene expression of samples from 30 different AML patients with acute myeloid leukemia in order to identify reliable gene expression markers for drug sensitivity.

本研究展示了一种颇具前景的方法，可用于识别急性髓系白血病（acute myeloid leukemia, AML）靶向治疗所需的稳健分子标志物。本研究证实，相较于多款当前主流前沿方法，本方法在识别验证数据中可复现的分子标志物以及精准预测药物敏感性方面表现更优。最后，本研究在AML中确认SMARCA4为拓扑异构酶II抑制剂（涵盖米托蒽醌与依托泊苷）敏感性的标志物及敏感性调控驱动因子，并证实：经转导后高表达SMARCA4的细胞系对上述药物的敏感性显著提升。实验设计概述：为识别可用于药物敏感性预测的可靠基因表达标志物，本研究对30名不同AML患者的样本开展了基因表达量检测。