Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data
复制标题
使用药物筛选数据对异质癌细胞群进行表型解卷积
DOI：
10.1016/j.crmeth.2023.100417
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Schjesvold, Fredrik
中科院分区：
文献类型：
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作者：
Köhn-Luque, Alvaro;Myklebust, Even Moa;Tadele, Dagim Shiferaw;Giliberto, Mariaserena;Schmiester, Leonard;Noory, Jasmine;Harivel, Elise;Arsenteva, Polina;Mumenthaler, Shannon M.;Schjesvold, Fredrik
肿瘤异质性是癌症治疗失败的重要驱动因素，因为治疗通常选择促进肿瘤生长和复发的耐药或耐药细胞亚群。使用传统基因组反卷积方法分析肿瘤样本的药物反应异质性产生的结果有限，部分原因是基因组变异和功能特征之间的映射不完善。在这里，我们利用机械群体模型开发一个统计框架，用于从大块肿瘤样本的标准药物筛选数据中分析表型异质性。这种称为 PhenoPop 的方法可以可靠地识别表现出不同药物反应的肿瘤亚群，并估计它们在总体群体中的药物敏感性和频率。我们将 PhenoPop 应用于合成生成的细胞群、混合细胞系实验和多发性骨髓瘤患者样本，并演示它如何在候选疗法下提供肿瘤生长的个体化预测。该方法还可以应用于癌症药物反应之外的各种生物环境中的反卷积问题。
Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.