Cells are complex systems in which many functions are performed by different genetically-defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we developed a Functional Module States framework. Using this framework, we 1) defined the drug induced transcriptional state space for breast cancer cell lines using large public gene expression datasets, and revealed that the transcriptional states are associated with drug concentration and drug targets; 2) identified potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown associated cancer dependency; and 3) used functional module states to predict transcriptional state dependent drug sensitivity and built prediction models using the functional module states for drug response. This approach demonstrates a similar prediction performance as do approaches using high dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

中文翻译：

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功能模块状态框架揭示了用于药物和靶标预测的细胞状态

细胞是复杂的系统，其中许多功能由不同的基因定义和编码的功能模块执行。为了系统地了解这些模块如何响应药物或遗传干扰，我们开发了功能模块状态框架。使用此框架，我们1）使用大型公共基因表达数据集定义了乳腺癌细胞系药物诱导的转录状态空间，并揭示了转录状态与药物浓度和药物靶标相关；2）通过综合分析药物治疗后的转录状态以及与基因敲低相关的癌症依赖性，确定潜在的潜在脆弱性；3）使用功能模块状态来预测转录状态依赖性药物敏感性，并使用功能模块状态来建立药物反应的预测模型。该方法显示出与使用高维基因表达值的方法相似的预测性能，并具有更清楚地揭示生物学相关转录状态和关键调节子的额外优势。