Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia
Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia
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DOI：
10.1158/2159-8290.cd-13-0350
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发表时间：
2013-12-01
期刊：
影响因子：
28.2
通讯作者：
Wennerberg, Krister
中科院分区：
文献类型：
--
作者：
Pemovska, Tea;Kontro, Mika;Wennerberg, Krister
我们提出了一种个性化的系统医学(ISM)方法来优化癌症药物治疗，每次一个患者。ISM的基础是(I)患者癌细胞对187种肿瘤药物的分子图谱和体外药物敏感性和耐药性测试(DSRT)，(Ii)预计有效的治疗的临床实施，以及(Iii)研究接受治疗的患者的连续样本，以了解耐药性的基础。在这里，应用ISM对28例急性髓系白血病(AML)患者的样本进行ISM分析，发现了基于DSRT图谱的五种主要分类药物反应亚型，其中一些具有明显的基因组特征(例如，IV亚组的MLL基因融合和V亚组的Flt3-ITD突变)。基于DSRT的治疗产生了几种临床反应。在DSRT引导的治疗下进展后，AML细胞显示出显著的克隆进化和新的基因组变化，潜在地解释了耐药性，而体外DSRT数据显示了对临床应用的药物的耐药性和对先前无效药物的新的脆弱性。这种方法可以促进已批准的靶向药物的系统性药物重新定位，并有助于确定临床测试的新药物的优先顺序和降低风险。(C)2013年AACR。
We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response sub-types based on DSRT profiles, some with distinct genomic features (e. g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.SIGNIFICANCE: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. (C) 2013 AACR.