A Functional Module States Framework Reveals Transcriptional States for Drug and Target Prediction.
Breast Neoplasms; Female; Gene Expression Profiling; Humans; Machine Learning; Molecular Targeted Therapy; Transcriptome; washington; seattle; isb; cell states; drug response prediction; functional states; machine learning; 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 develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.
Institute for Systems Biology
Qin, Guangrong; Knijnenburg, Theo A; Gibbs, David L; Moser, Russell; Monnat, Raymond J; Kemp, Christopher J; and Shmulevich, Ilya, "A Functional Module States Framework Reveals Transcriptional States for Drug and Target Prediction." (2022). Articles, Abstracts, and Reports. 5707.