A functional module states framework reveals cell states for drug and target prediction

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.