Network-based analysis with primary cells reveals drug response landscape of acute myeloid leukemia
Network-based analysis with primary cells reveals drug response landscape of acute myeloid leukemia
复制标题
基于网络的原代细胞分析揭示了急性髓系白血病的药物反应情况
DOI：
10.1016/j.yexcr.2020.112054
复制
发表时间：
2020-08-01
影响因子：
3.7
通讯作者：
Liu, Qingsong
中科院分区：
文献类型：
--
作者：
Chen, Cheng;Wang, Li;Liu, Qingsong
急性髓系白血病（AML）是成人最常见、最复杂、最异质的血液系统恶性肿瘤之一。尽管在理解AML的病理学方面取得了进展，但与CML、CLL等相比，5年生存率仍然较低。基因组特征与药物反应之间的关系对于精确用药至关重要。在此，我们描绘了145种药物对33种来源于AML患者的原代细胞样品的反应的图片，其具有通过全外显子测序和RNA测序评估的全谱基因组特征。总体而言，大多数样本对联合化疗方案的敏感性远高于单一化疗药物。总体而言，这些样本对中药（TCM）和靶向药物中度敏感。在加权基因共表达网络分析（WGCNA）中，中医药和靶向治疗在基因模块相关性方面显示出相似的遗传特征。同时，在这些不同类型的治疗中，miRNAs、lncRNAs和mRNAs的表达没有显示出明显的基因模块相关性。此外，联合化疗比单药化疗具有更多的模块相关性。有趣的是，我们发现基因突变和药物反应在任何WGCNA模块分析中都没有富集。大多数敏感的药物反应生物标志物富集在核糖体、内吞作用、细胞周期和p53相关信号通路中。这项研究表明，基因表达模块可能比基因突变显示出更好的相关性，用于药物疗效预测。
Acute myeloid leukemia (AML) is one of the most common, complex, and heterogeneous hematological malignancies in adults. Despite progresses in understanding the pathology of AML, the 5-year survival rates still remain low compared with CML, CLL, etc. The relationship between genomic features and drug responses is critical for precision medication. Herein, we depicted a picture for response of 145 drugs against 33 primary cell samples derived from AML patients with full spectrum of genomic features assessed by whole exon sequencing and RNA sequencing. In general, most of the samples were much more sensitive to the combinatorial chemotherapy regimens than the single chemotherapy drugs. Overall, these samples were moderately sensitive to the Traditional Chinese Medicine (TCM) and the targeted drugs. In the weighted gene coexpression network analysis (WGCNA), the TCM and targeted therapies displayed similar genetic signatures in the gene module correlation. Meanwhile, the expression of miRNAs, lncRNAs, and mRNAs did not display apparent gene module correlations among those different types of therapies. In addition, the combinatorial chemotherapy bear more module correlations than the single drugs. Interestingly, we found that the gene mutations and drug response were not enriched in any WGCNA module analysis. Most of the sensitive drug response biomarkers were enriched in the ribosome, endocytosis, cell cycle, and p53 associated signaling pathways. This study showed that gene expression modules might show better correlation than gene mutations for drug efficacy predictions.