Significance: The efficacy of anticancer drugs has been conventionally estimated in the laboratory by measuring post-treatment in vitro proliferation rates of cancer cell lines. The results of such in vitro measurements, however, rarely translate to human trials, thus posing a significant translational challenge. Additional cellular phenotypes have hence been studied in recent years, examining cell migration and invasion. Understanding how each of these in vitro measured phenotypes contributes to the patient response and survival is hence an important open challenge. Here, mining thousands of breast cancer tumors and cell-line experiments, we explore this relationship and delineate the individual contributions of these phenotypes, in predicting patient survival and response. Methods: Migration and proliferation was measured for 43 different breast cancer cell lines. Integrating these measurements with the cell lines' transcriptomics, we built gene expression-based predictors of each of these phenotypes in cell lines and in tumors. The predicted phenotypes were then used to study their contribution to patient survival. Results: Analyzing the transcriptomics of these cell lines, we identified specific gene-expression signatures of breast cancer migration and proliferation, that are highly predictive of these phenotypes (using cross validation). Subsequently, we applied these signatures to a collection of more than 2800 breast cancer tumors in the TCGA and METABRIC collection, to predict their proliferation and migration rates. Our analysis shows that both laboratory-measured proliferation and migration signatures are predictive of breast cancer stage, grade, subtypes, and finally, of patient survival. Notably, we find that the predicted migration rates of tumors are stronger predictors of patient survival than their predicted proliferation rates. This finding is further reinforced via analyzing migration and proliferation signatures that we derive from in vitro shRNA knockout experiments. We also find that patients whose tumors have high predicted migration rates specifically benefited from cytoskeletal drug treatments. Finally, we find that the predicted migration rates are associated with response of checkpoint inhibitor in patients. We are further extensively validating this by collecting tumor biopsies from patients post immunotherapy. Conclusions: Taken together, these results testify to the superiority of migration- over proliferation-based transcriptomic signatures in predicting breast cancer tumor phenotypes and patients’ survival. This suggests that in vitro migration measurements of drug response may significantly increase the translational value of cellular phenotypic measurements in predicting drug efficacy in patients. Finally, because tumor migration rates are predictive of cancer immunotherapy, they may provide a viable biomarker for immunotherapy response in patients.

Citation Format: Nishanth U. Nair, Avinash Das, Joo Sang Lee, Sridhar Hannenhalli, Sylvia Le Dévédec, Bob van de Water, Eytan Ruppin. Cell migration is a stronger predictor of patient survival in breast cancer than cell proliferation [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A023.