A Text-Based Computational Framework for Patient-Specific Modeling for Classification of Cancers

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment are urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on gene regulation into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 breast cancer patients using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, Extracellular Signal-Regulated Kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.