Following primary treatment for breast cancer, residual tumor cells can lie dormant in the body for decades before giving rise to recurrent tumors, which are invariably fatal. To date, little is known about the biology of breast cancer recurrence, as well as the biology of how the dormant state is maintained. This indicates a need for further study of breast cancer dormancy and recurrence. In this work, we focus on dormancy. Our laboratory has previously developed and utilized a murine cell line, 54074, as a model for studying cellular dormancy in vitro — a system that we refer to here as in vitro dormancy (IVD). However, while this model has been demonstrated to faithfully recapitulate a number of cellular and molecular processes observed in their corresponding in vivo dormancy models, the IVD model remains poorly characterized. In particular, relatively little is known about the biological processes that occur in the IVD system, despite the ability to easily manipulate a variety of experimental variables of interest, thus precluding high-throughput approaches to discovery science. The present work addresses this gap by describing our development of a novel computational pipeline, which we term “Word Cloud,” for identifying differentially regulated biological pathways in a screen-like manner using IVD gene expression data that has undergone initial processing through gene set enrichment analysis (GSEA). We show by western blotting and immunofluorescence for signal pathway activity that several of the resulting predictions made using the Word Cloud approach are accurate. Further, by comparing Word Cloud results from the IVD system with those from gene expression studies of breast cancer patients treated with neoadjuvant therapy, we demonstrate that this method can enable elucidation of biological processes that are shared by dormant human and mouse breast cancer cells.