Warchal Scott J, Dawson John C, Carragher Neil O
Institute of Genetics and Molecular Medicine, Cancer Research UK Edinburgh Centre, University of Edinburgh , Edinburgh, United Kingdom .
Assay Drug Dev Technol. 2016 Sep;14(7):395-406. doi: 10.1089/adt.2016.730.
In this article, we have developed novel data visualization tools and a Theta comparative cell scoring (TCCS) method, which supports high-throughput in vitro pharmacogenomic studies across diverse cellular phenotypes measured by multiparametric high-content analysis. The TCCS method provides a univariate descriptor of divergent compound-induced phenotypic responses between distinct cell types, which can be used for correlation with genetic, epigenetic, and proteomic datasets to support the identification of biomarkers and further elucidate drug mechanism-of-action. Application of these methods to compound profiling across high-content assays incorporating well-characterized cells representing known molecular subtypes of disease supports the development of personalized healthcare strategies without prior knowledge of a drug target. We present proof-of-principle data quantifying distinct phenotypic response between eight breast cancer cells representing four disease subclasses. Application of the TCCS method together with new advances in next-generation sequencing, induced pluripotent stem cell technology, gene editing, and high-content phenotypic screening are well placed to advance the identification of predictive biomarkers and personalized medicine approaches across a broader range of disease types and therapeutic classes.
在本文中,我们开发了新颖的数据可视化工具和Theta比较细胞评分(TCCS)方法,该方法支持通过多参数高内涵分析对多种细胞表型进行高通量体外药物基因组学研究。TCCS方法提供了不同细胞类型之间化合物诱导的不同表型反应的单变量描述符,可用于与遗传、表观遗传和蛋白质组数据集进行关联,以支持生物标志物的鉴定并进一步阐明药物作用机制。将这些方法应用于跨高内涵分析的化合物谱分析,这些分析纳入了代表疾病已知分子亚型的特征明确的细胞,有助于在无需事先了解药物靶点的情况下制定个性化医疗策略。我们展示了原理验证数据,量化了代表四种疾病亚类的八种乳腺癌细胞之间不同的表型反应。TCCS方法与下一代测序、诱导多能干细胞技术、基因编辑和高内涵表型筛选方面的新进展相结合,非常适合在更广泛的疾病类型和治疗类别中推进预测性生物标志物的鉴定和个性化医疗方法的发展。