Survival Outcomes in Cancer Patients Predicted by a Partial EMT Gene Expression Scoring Metric

Metastasis is a significant contributor to morbidity and mortality for many cancer patients and remains a major obstacle for effective treatment. In many tissue types, metastasis is fueled by the epithelial-to-mesenchymal transition (EMT)-a dynamic process characterized by phenotypic and morphologic changes concomitant with increased migratory and invasive potential. Recent experimental and theoretical evidence suggests that cells can be stably halted en route to EMT in a hybrid E/M phenotype. Cells in this phenotype tend to move collectively, forming clusters of circulating tumor cells that are key tumor-initiating agents. Here, we developed an inferential model built on the gene expression of multiple cancer subtypes to devise an EMT metric that characterizes the degree to which a given cell line exhibits hybrid E/M features. Our model identified drivers and fine-tuners of epithelial-mesenchymal plasticity and recapitulated the behavior observed in multiple experiments across cancer types. We also predicted and experimentally validated the hybrid E/M status of certain cancer cell lines, including DU145 and A549. Finally, we demonstrated the relevance of predicted EMT scores to patient survival and observed that the role of the hybrid E/M phenotype in characterizing tumor aggressiveness is tissue and subtype specific. Our algorithm is a promising tool to quantify the EMT spectrum, to investigate the correlation of EMT score with cancer treatment response and survival, and to provide an important metric for systematic clinical risk stratification and treatment. .