颜林林
(2022-08-12 07:42):
#paper doi:10.1016/j.ccell.2022.07.002 Cancer Cell, 2022, Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. 这是一项关于AML（急性骨髓性白血病）的长达10年的真实世界临床研究，收集了来自多个中心的 805 名患者（942 个样本），对样本进行基因组和转录组的测序，同时使用离体细胞培养进行药物反应实验，此外还利用NLP技术整理和分析患者的病历数据。在数据分析方面，使用反卷积方法，通过转录组数据推断出样本的细胞类群组成，并结合临床信息和组学数据分析结果，识别出影响药物响应情况的因素（如年龄、基因表达、细胞分化状态等）。所建立的模型，揭示了单个基因 PEAR1 是患者生存的最强预测因子之一。所形成的数据集，也提供了一个在线交互式网站进行分析展示。分析方面基本都是很多生信数据挖掘类文章的常见套路，并没有特别新颖之处，但得益于长时间积累的队列及其完整的临床信息，作为一个重要的数据集资源，以及单病种的真实世界研究实例，也还是很有价值的。此外，关于药物响应的细胞实验部分相对独立，与患者预后进行关联解释并不容易，大概也是为了提升文章份量而加入的。
Integrative analysis of drug response and clinical outcome in acute myeloid leukemia
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Abstract:
Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.
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